R Notebook - Treponema UK UKHSA-cohort Analysis 2022. Revision 04-2023
Make a clean environment
rm(list=ls())
Load packages
packages.list <- c("ggplot2","treeio","ggtree","ggnewscale","ape","dplyr","tidyverse","tidyr","phytools","RColorBrewer","lubridate","readxl","ggforce","ggstance","ggridges","cowplot","hexbin","scales","haven","network","ggnetwork","intergraph","igraph","ggraph","graphlayouts","scatterpie","maps","mapdata","maptools","rgdal","rgeos","broom","ggrepel","ggridges","magick","ggbeeswarm","ggrastr")
#"plyr","Cairo","ggmap","emojifont","rPinecone","pairsnp","CoordinateCleaner","gridExtra","dendextend","ggdendro",
#BiocManager::install("ggtree")
#BiocManager::install("treeio")
for(pkg in packages.list){
eval(bquote(library(.(pkg)))) }
Confirm current environmental setup
R.Version()$platform
[1] "x86_64-apple-darwin17.0"
$arch
[1] "x86_64"
$os
[1] "darwin17.0"
$system
[1] "x86_64, darwin17.0"
$status
[1] ""
$major
[1] "4"
$minor
[1] "1.2"
$year
[1] "2021"
$month
[1] "11"
$day
[1] "01"
$`svn rev`
[1] "81115"
$language
[1] "R"
$version.string
[1] "R version 4.1.2 (2021-11-01)"
$nickname
[1] "Bird Hippie"print(sessionInfo())R version 4.1.2 (2021-11-01)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Monterey 12.6.1
Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
locale:
[1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] gtable_0.3.1 randomcoloR_1.1.0.1 fastbaps_1.0.6 rPinecone_0.1.0 devtools_2.4.4
[6] usethis_2.1.6 ggrastr_1.0.1 ggbeeswarm_0.6.0 magick_2.7.3 ggrepel_0.9.1
[11] broom_1.0.0 rgeos_0.5-9 rgdal_1.5-30 maptools_1.1-4 sp_1.4-6
[16] mapdata_2.3.0 scatterpie_0.1.7 graphlayouts_0.7.2 ggraph_2.0.5 igraph_1.3.5
[21] intergraph_2.0-2 ggnetwork_0.5.10 network_1.17.1 haven_2.4.3 scales_1.2.1
[26] hexbin_1.28.2 ggridges_0.5.3 lubridate_1.8.0 RColorBrewer_1.1-3 ggnewscale_0.4.5
[31] cowplot_1.1.1 treemapify_2.5.5 treeio_1.18.1 ggtree_3.2.1 phytools_0.7-90
[36] maps_3.4.0 ape_5.6-2 readxl_1.3.1 ggforce_0.3.3 ggstance_0.3.5
[41] forcats_0.5.1 stringr_1.4.1 dplyr_1.0.7 purrr_0.3.4 readr_2.1.3
[46] tidyr_1.1.4 tibble_3.1.8 ggplot2_3.3.6 tidyverse_1.3.2 vcfR_1.13.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 clipr_0.8.0 tidyselect_1.2.0 htmlwidgets_1.5.4
[5] grid_4.1.2 combinat_0.0-8 Rtsne_0.16 munsell_0.5.0
[9] codetools_0.2-18 ragg_1.2.2 miniUI_0.1.1.1 withr_2.5.0
[13] colorspace_2.0-3 knitr_1.40 rstudioapi_0.13 stats4_4.1.2
[17] labeling_0.4.2 RgoogleMaps_1.4.5.3 mnormt_2.0.2 polyclip_1.10-0
[21] farver_2.1.1 coda_0.19-4 vctrs_0.5.0 generics_0.1.1
[25] clusterGeneration_1.3.7 xfun_0.34 R6_2.5.1 bitops_1.0-7
[29] cachem_1.0.6 gridGraphics_0.5-1 assertthat_0.2.1 promises_1.2.0.1
[33] pinfsc50_1.2.0 googlesheets4_1.0.0 beeswarm_0.4.0 Cairo_1.5-12.2
[37] processx_3.7.0 phangorn_2.8.0 tidygraph_1.2.0 rlang_1.0.6
[41] systemfonts_1.0.4 scatterplot3d_0.3-41 RcppRoll_0.3.0 splines_4.1.2
[45] lazyeval_0.2.2 gargle_1.2.0 yaml_2.3.6 reshape2_1.4.4
[49] modelr_0.1.8 backports_1.4.0 httpuv_1.6.6 tools_4.1.2
[53] ggplotify_0.1.0 statnet.common_4.5.0 ellipsis_0.3.2 jquerylib_0.1.4
[57] BiocGenerics_0.40.0 sessioninfo_1.2.2 Rcpp_1.0.9 plyr_1.8.6
[61] prettyunits_1.1.1 ps_1.7.1 viridis_0.6.2 urlchecker_1.0.1
[65] deSolve_1.33 S4Vectors_0.32.4 ggmap_3.0.0 cluster_2.1.2
[69] fs_1.5.2 magrittr_2.0.3 sna_2.6 reprex_2.0.1
[73] mvtnorm_1.1-3 googledrive_2.0.0 tmvnsim_1.0-2 pkgload_1.3.0
[77] xtable_1.8-4 mime_0.12 hms_1.1.2 patchwork_1.1.1
[81] evaluate_0.17 jpeg_0.1-9 IRanges_2.28.0 gridExtra_2.3
[85] compiler_4.1.2 V8_4.2.1 crayon_1.5.2 htmltools_0.5.3
[89] later_1.3.0 ggfun_0.0.4 mgcv_1.8-38 tzdb_0.3.0
[93] aplot_0.1.1 expm_0.999-6 DBI_1.1.1 tweenr_1.0.2
[97] subplex_1.8 dbplyr_2.1.1 MASS_7.3-54 Matrix_1.3-4
[101] ade4_1.7-19 permute_0.9-7 cli_3.4.1 quadprog_1.5-8
[105] parallel_4.1.2 pkgconfig_2.0.3 geosphere_1.5-14 numDeriv_2016.8-1.1
[109] foreign_0.8-81 xml2_1.3.3 memuse_4.2-1 vipor_0.4.5
[113] bslib_0.4.0 rvest_1.0.2 yulab.utils_0.0.4 callr_3.7.2
[117] digest_0.6.30 vegan_2.6-2 rmarkdown_2.17 cellranger_1.1.0
[121] fastmatch_1.1-3 tidytree_0.3.6 datapasta_3.1.0 curl_4.3.3
[125] pairsnp_0.1.0 shiny_1.7.2 geiger_2.0.10 rjson_0.2.20
[129] lifecycle_1.0.3 nlme_3.1-153 jsonlite_1.8.3 seqinr_4.2-16
[133] viridisLite_0.4.1 fansi_1.0.3 pillar_1.8.1 lattice_0.20-45
[137] pkgbuild_1.3.1 fastmap_1.1.0 httr_1.4.4 plotrix_3.8-2
[141] remotes_2.4.2 glue_1.6.2 png_0.1-7 profvis_0.3.7
[145] stringi_1.7.8 sass_0.4.2 ggfittext_0.9.1 textshaping_0.3.6
[149] memoise_2.0.1
Make some shortcuts for plotting
y.theme.strip <- theme(axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y= element_blank())
y.theme.strip.partial <- theme(axis.text.y = element_blank(), axis.ticks.y= element_blank())
x.theme.strip <- theme(axis.title.x = element_blank(), axis.text.x = element_blank(), axis.ticks.x= element_blank())
x.theme.strip.partial <- theme(axis.text.x = element_blank(), axis.ticks.x= element_blank())
x.theme.strip.labs <- theme(axis.text.x = element_blank(),axis.title.x = element_blank())
x.theme.axis.rotate <- theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
legend.strip <- theme(legend.position = "none")
theme.text.size <- theme(text = element_text(size = 10))
'%notin%' <- Negate('%in%')
max.font.size <- 7
basic.font.size <- 6
min.font.size <- 5.25
theme.text.size <- theme(text = element_text(size = basic.font.size))
theme.text.size.within <- (5/14)*min.font.size
panel.lab.size <- 10
Specify raw data - global dataset
#Data_input_directory <- "/Users/mb29/Papers/Treponema_UK-PHE-gen-epi_2021/Data/"
#Data_input_directory <- "/Users/mb29/Papers/Treponema_UK-PHE-gen-epi_2021/Rnotebook/Rnotebook_09-2022/data/"
Data_input_directory <- paste0(getwd(), "/inputdata/")
################################
#### Tree data
# ML tree (refined dataset)
TPA.MLtree.file <- paste0(Data_input_directory,"TPA-uber.remasked.2020-11-10.goodcov25.gubbins.SNPs.aln.renamed.fix-zero-dist.treefile")
# Pyjar tree (refined dataset)
TPA.pyjar.file <- paste0(Data_input_directory,"TPA-uber.remasked.2020-11-10.goodcov25.gubbins.SNPs.aln.renamed.pyjar.tre")
# Full size BEAST2 analysis - previously generated as part of Beale, 2021.
full.beast2.tree.file <- paste0(Data_input_directory,"TPA-uber_beast2_strict-skyline-500M_10pop_consensus.tree")
# Ancestral reconstruction of global TPA ML tree from TreeTime (refined dataset)
TPA.treetime.ancestral.tree.file <- paste0(Data_input_directory,"TPA.annotated_tree.fix-hung.nexus")
TPA.treetime.ancestral.vcf.file <- paste0(Data_input_directory,"TPA-uber.midpoint.ancestral_sequences.fix-hung.vcf")
# Functionally annotated variants, extracted from snpEff vcf into tsv using snpSift
TPA.snpEff.file <- paste0(Data_input_directory,"TPA-uber.midpoint.ancestral_sequences.relab.bcf.ann.vcf.vartab.sepline.tsv")
# Gff file for SS14 reference genome, containing gene positions/annotations
SS14.gff.file <- paste0(Data_input_directory,"Treponema_pallidum_subs._pallidum_SS14.NC_021508.1.2021-06-13.gff")
################################
#### Meta data
# Supplement from TPA-Uber paper - Beale, 2021
TPA.meta2.file <- paste0(Data_input_directory,"Sup_Data1_Global_Sample-Metadata__09-2022.xlsx")
# England specific metadata collated by PHE/UKHSA
PHE.metadata.linked.file <- paste0(Data_input_directory,"Sup_Data2_TPA.UK-only.PHE.metadata.2022-02-02.xlsx")
# England specific mapping shapefile data with Public Health Boundaries
# Imported datafile from https://geoportal.statistics.gov.uk/datasets/public-health-england-centres-december-2016-full-clipped-boundaries-in-england/explore?location=52.950000%2C-2.000000%2C6.88
UK.publichealth.shapefile.data <- paste0(Data_input_directory,"Public_Health_England_Centres_(December_2016)_Boundaries")
################################
#### Externally plotted figures (e.g. GrapeTree) for inclusion in multipanel figures
# Externally plotted grapetree minimum spanning tree for whole of England - code to extract subtree that was used to make this is included later in this Rnotebook
TPA.UK.Grapetree.sublineages.file <- paste0(Data_input_directory,"TPA-UK-2022-02-03.sublineage-MSTree.Inkscaped.svg")
# Externally plotted grapetree minimum spanning tree for whole of England - 3-variable plots
TPA.UK.Grapetree.3way.file <- paste0(Data_input_directory,"TPA-UK-2022-02-16.-MSTree_3-way-figure.Inscaped-3.svg")
# Externally plotted grapetree minimum spanning tree for whole of England - HIV status
TPA.UK.Grapetree.HIV.file <- paste0(Data_input_directory,"TPA-UK-2022-02-03.HIVstatus-MSTree_inkscaped.svg")
# Externally plotted grapetree minimum spanning tree for North East England networks
TPA.NorthEastEngland.Grapetree.file <- paste0(Data_input_directory,"TPA-UK-NorthEast-2022-02-26.GenderOrientation-MSTree.inkscaped.+node-counts+GBMSM.svg")
Specify directory to output plots
Figure_output_directory
[1] "/Users/mb29/Papers/Treponema_UK-PHE-gen-epi_2021/Github/Syphilis_Genomic_Epi_England_2022-23/Figures_revision_03-2023/"
Read in trees
TPA.MLtree <- midpoint.root(read.tree(TPA.MLtree.file))
TPA.pyjar.tree <- midpoint.root(read.tree(TPA.pyjar.file))
Read in final output metadata from Global Uber study (Beale 2021)
TPA.meta2.1 <- readxl::read_excel(TPA.meta2.file,sheet="Supplementary_Data1_Sample-Meta")
Create a colour scheme for Lineages, Countries and Continents (consistent with Beale, 2021)
# Colouring for country
continental.country.cols.brew2 <- unique(TPA.meta2.1[,c("Geo_Country","Continent")])
continental.country.cols.brew2 <- continental.country.cols.brew2[order(continental.country.cols.brew2$Continent,continental.country.cols.brew2$Geo_Country),]
continental.country.cols.brew2$country.col <- c("#ec7014","#fec44f","#de2d26","#fb6a4a","#bdbdbd","#737373",brewer.pal(n=8,"Purples")[4:8],brewer.pal(n=8,"Blues")[3:8],brewer.pal(n=5,"Greens")[3:5],"#c51b8a","#8c510a")
# Colouring for Continent
continental.cols.brew2 <- data.frame(Continent=sort(unique(TPA.meta2.1$Continent)),stringsAsFactors=F)
continental.cols.brew2$continent.col <- c("#fec44f","#de2d26","#bdbdbd","#2171b5","#74c476","#c51b8a","#ec7014")
# Colouring for TPA Lineage
TPA_Lineage.cols <- data.frame(Lineage=sort(unique(TPA.meta2.1$TPA_Lineage)),stringsAsFactors=F)
TPA_Lineage.cols$Lineage.col <- c("royalblue2", "indianred1")
#c("#436eee", "#666666","#ff6a6a")
TPA_Lineage.cols$Lineage <- factor(TPA_Lineage.cols$Lineage, levels=c("Nichols","SS14","outlier"))
# Lineage Hexcodes
# royalblue2 #436eee
# indianred1 #ff6a6a
Define colours for sublineages
# Define sublineage clustering scheme using brew colourscales
sublineages.cols.brew <- data.frame(unique(TPA.meta2.1[,c("TPA_Lineage","TPA.pinecone.sublineage")]), stringsAsFactors = F)
sublineages.cols.brew <- sublineages.cols.brew[order(sublineages.cols.brew$TPA_Lineage,sublineages.cols.brew$TPA.pinecone.sublineage),]
sublineages.cols.brew$sublin.order <- as.numeric(as.character(sublineages.cols.brew$TPA.pinecone.sublineage))Warning: NAs introduced by coercionsublineages.cols.brew <- sublineages.cols.brew[order(sublineages.cols.brew$sublin.order),]
# For revised bootstrapped clusters
sublineages.cols.brew$sublineage.cols <- c("#FC9272","#EF3B2C",brewer.pal(n=4,"Greens")[2:4],brewer.pal(n=4,"YlOrBr")[c(2,3)],brewer.pal(n=6,"Blues")[2:6],brewer.pal(n=6,"Purples")[2:6],"grey80","grey80","grey80","grey80")
sublineages.cols.brew <- unique(sublineages.cols.brew[,c("TPA.pinecone.sublineage","sublineage.cols")])
sublineages.cols.brew <- sublineages.cols.brew[order(as.numeric(as.character(sublineages.cols.brew$TPA.pinecone.sublineage))),]Warning in order(as.numeric(as.character(sublineages.cols.brew$TPA.pinecone.sublineage))) :
NAs introduced by coercionsublineages.cols.brew$TPA.pinecone.sublineage <- factor(sublineages.cols.brew$TPA.pinecone.sublineage, levels=sublineages.cols.brew$TPA.pinecone.sublineage)
sublineages.cols.brew <- sublineages.cols.brew[!is.na(sublineages.cols.brew$sublineage),]
colnames(sublineages.cols.brew) <- c("sublineage","sublineage.cols")
sublineages.cols.brew <- unique(sublineages.cols.brew)
Restrict analysis to high quality genomes (and tree)
TPA.meta2.1 <- TPA.meta2.1[TPA.meta2.1$finescale.analysis=="Yes",]
Create a “UK” variable, and a “PHE” variable
TPA.meta2.1$is.UK <- ifelse(TPA.meta2.1$Geo_Country=="UK","UK","Other")
TPA.meta2.1$is.PHE <- ifelse(TPA.meta2.1$Geo_Country=="UK" & grepl("PHE",TPA.meta2.1$Sample_Name),"PHE","Other")
# Prepare ML tree
TPA.MLtree.ggtree <- ggtree(TPA.MLtree,layout = "fan",open.angle = 10, right=T)Scale for 'y' is already present. Adding another scale for 'y', which will replace the existing scale.# Prepare country dataset
TPA.rawseq.countries.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Country=TPA.meta2.1$Geo_Country, stringsAsFactors = F)
# Prepare continent dataset
TPA.rawseq.continents.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Continent=TPA.meta2.1$Continent, stringsAsFactors = F)
# Prepare UK data strip
TPA.rawseq.UK.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, England=TPA.meta2.1$is.UK, stringsAsFactors = F)
TPA.rawseq.UK.p[TPA.rawseq.UK.p$England=="UK",] <- "England"
# Prepare PHE data strip
TPA.rawseq.PHE.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, PHE=TPA.meta2.1$is.PHE, stringsAsFactors = F)
# Prepare Major lineage dataset
TPA.rawseq.Lineage.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Lineage=TPA.meta2.1$TPA_Lineage, stringsAsFactors = F)
# Prepare sublineage lineage dataset
TPA.rawseq.subLineage.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Sublineage=TPA.meta2.1$TPA.pinecone.sublineage, stringsAsFactors = F)
# Prepare Year dataset (all samples)
TPA.rawseq.all.Years.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Year=TPA.meta2.1$Sample_Year, stringsAsFactors = F)
floor_5years <- function(value){ return(value - value %% 5) }
TPA.meta2.1$Sample_5year.window <- paste0(floor_5years(as.numeric(TPA.meta2.1$Sample_Year)),"-",floor_5years(as.numeric(TPA.meta2.1$Sample_Year))+5)Warning in floor_5years(as.numeric(TPA.meta2.1$Sample_Year)) :
NAs introduced by coercion
Warning in floor_5years(as.numeric(TPA.meta2.1$Sample_Year)) :
NAs introduced by coercion# Some samples have uncertain dates (up to 20-30 years uncertainty), but for the purposes of these plotting categories we'll use the centrepoint year
TPA.meta2.1$Sample_5year.window <- sapply(1:nrow(TPA.meta2.1), function(x) ifelse(TPA.meta2.1$Sample_Year[x]=="-",NA, ifelse(is.na(TPA.meta2.1$Sample_5year.window[x]),NA, ifelse(TPA.meta2.1$Sample_Year[x]=="1950-1980","1965-1970",ifelse(TPA.meta2.1$Sample_Year[x]=="1960-1980","1965-1970" ,ifelse(TPA.meta2.1$Sample_Year[x]=="1980-1999","1985-1990",TPA.meta2.1$Sample_5year.window[x]))))))
TPA.meta2.1$Sample_year.1990.cuttoff <- ifelse(TPA.meta2.1$Sample_Year>1990,TPA.meta2.1$Sample_Year,"<1990")
TPA.meta2.1$Sample_year.1999.cuttoff <- ifelse(TPA.meta2.1$Sample_Year>1999,TPA.meta2.1$Sample_Year,"<1999")
TPA.rawseq.year.cuttoff.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Sample.Year=TPA.meta2.1$Sample_year.1999.cuttoff, stringsAsFactors = F)
# Bring in PHE metadata
PHE.metadata.linked <- readxl::read_excel(PHE.metadata.linked.file)
Do some cleanup and factoring of variables
PHE.metadata.linked$age_group <- factor(PHE.metadata.linked$age_group, levels=rev(c("16-24","25-34","35-44","45+","Unknown")))
PHE.metadata.linked$london <- factor(PHE.metadata.linked$london,levels=rev(c("Yes","No","Unknown")))
PHE.metadata.linked$ukborn <- factor(PHE.metadata.linked$ukborn,levels=rev(c("Yes","No","Unknown")))
PHE.metadata.linked$hivpos <- factor(PHE.metadata.linked$hivpos, levels=rev(c("Yes","No","Unknown")))
# need to update terminology of 'MSM' to 'GBMSM'
PHE.metadata.linked[PHE.metadata.linked$gender_orientation=="MSM","gender_orientation"] <- "GBMSM"
PHE.metadata.linked$gender_orientation <- factor(PHE.metadata.linked$gender_orientation, levels=rev(c("MSW","GBMSM","WSM","MUnknown","Unknown")))
PHE.metadata.linked$phe_centre <- factor(PHE.metadata.linked$phe_centre, levels=rev(c("East Midlands", "East of England", "London", "North East", "North West", "South East", "South West", "West Midlands", "Yorkshire and Humber", "UK (not England)", "Not Known")))
PHE.metadata.linked$TPA.pinecone.sublineage <- factor(PHE.metadata.linked$TPA.pinecone.sublineage, levels=sublineages.cols.brew$sublineage)
### Extract information about duplicates
PHE.metadata.duplicates <- PHE.metadata.linked[!is.na(PHE.metadata.linked$dup_flag),]
PHE.metadata.duplicates <- PHE.metadata.duplicates[!is.na(PHE.metadata.duplicates$Sample_Name),]
PHE.patient.matches <- data.frame(
stringsAsFactors = FALSE,
dup_flag = c("1A","1B",
"2A","2B","3A","3B","4A",
"4B","5A","5B"),
dup_Patient = c("Patient 1",
"Patient 1","Patient 2",
"Patient 2","Patient 3","Patient 3",
"Patient 4","Patient 4",
"Patient 5","Patient 5"),
dup_Patient_Sample = c("sample 1",
"sample 2","sample 1",
"sample 2","sample 1","sample 2",
"sample 1","sample 2","sample 1",
"sample 2")
)
PHE.metadata.duplicates <- left_join(PHE.metadata.duplicates, PHE.patient.matches, by="dup_flag")
PHE.metadata.duplicatesDuplicate Samples missing metadata are all ‘new duplicates’ and were excluded due to low mapping coverage (all checked).
Samples labelled ‘ZA’ and ‘XB’ had duplicates in the original dataset, but the reciprocal pairs were excluded due to quality isues.
Available pairs - Patient 3, Patient 4
PHE.metadata.duplicates.paired <- PHE.metadata.duplicates[PHE.metadata.duplicates$dup_Patient %in% c("Patient 3","Patient 4"),]
PHE.metadata.duplicates.paired[order(PHE.metadata.duplicates.paired$dup_Patient, PHE.metadata.duplicates.paired$year,PHE.metadata.duplicates.paired$month),c("Sample_Name","dup_Patient", "month.fix", "year")]
These will be revisited later in the analysis.
Patient 4 HIV-ve MSM (45+), UK born, PHE region D 2 samples, collected in the same month and year Both samples are sublineage 1, and identical (0 pwSNPs) Likely the same infection (depending on dates, treatment, etc), but can’t rule out reinfection with same strain.
Patient 3 HIV-ve MSM (35-44), not UK born, based in London (C) 2 samples, collected 9 months apart Both samples are sublineage 1, but have 7 pairwise SNPs between them (loads!) Reinfection – probably from a different transmission network
However, based on the sample dates, as well as the outcome of the downstream genetic analysis, we can see that Patient 3 has duplicate infection events (different dates, 10 months apart) and the genomes are distinct (7 SNPs apart), whereas Patient 4 samples were collected in the same month and year (i.e. are likely duplicates from the same infection) and has identical genomes.
For downstream analysis purposes, we will retain both samples for Patient 3 (discrete infections), but exclude one sample from Patient 4 (duplicate infection samples) - ‘PHE150126A’ has much better genome coverage, so exclude ‘PHE150125A’
### Further Exclusions
PHE130056A - duplicate of PHE130057B (already removed, so not relevant) - don’t exclude! PHE170402A - quality control sample PHE170378A - quality control sample
Exclude duplicate sequences
duplicate.exclusion.list <- c("PHE150125A","PHE170402A","PHE170378A")
PHE.metadata.linked <- PHE.metadata.linked[PHE.metadata.linked$Sample_Name %notin% duplicate.exclusion.list,]
Moving on…
Define some colour schemes
# define some colors for each region
PHE.region.cols.brew <- data.frame(UKHSA.region=c("North East", "North West", "Yorkshire and Humber", "East Midlands", "West Midlands", "East of England", "London", "South East","South West","UK (not England)", "Not Known"), stringsAsFactors=F)
PHE.region.cols.brew$region.col <- c("#A6CEE3","#1F78B4","#CAB2D6","#33A02C","#B2DF8A","#FF7F00","#E31A1C","#FB9A99","#D4BB02","grey75","grey25")
# HIV color scheme
PHE.hiv.cols <- data.frame(hivpos=rev(sort(unique(PHE.metadata.linked$hivpos))), stringsAsFactors=F)
PHE.hiv.cols$hiv.cols <- c("#1f78b4","#b2df8a","grey75")
# Orientation colour scheme
PHE.orientation.cols <- data.frame(orientation=rev(sort(unique(PHE.metadata.linked$gender_orientation))), stringsAsFactors=F)
PHE.orientation.cols$orientation <- factor(PHE.orientation.cols$orientation, levels=rev(sort(unique(PHE.metadata.linked$gender_orientation))), labels=c("MSW","GBMSM","WSM","MUnknown","Unknown"))
PHE.orientation.cols$orientation.cols <- c("#1f78b4","#b2df8a","#fb9a99","#a6cee3","grey75")
# UK born colour scheme
PHE.ukborn.cols <- data.frame(ukborn=rev(sort(unique(PHE.metadata.linked$ukborn))),ukborn.cols=c("#1f78b4","#b2df8a","grey75"),stringsAsFactors = F)
# London based colour scheme
PHE.london.cols <- data.frame(london=rev(sort(unique(PHE.metadata.linked$london))),london.cols=c("#1f78b4","#b2df8a","grey75"),stringsAsFactors = F)
# Age group colour scheme
PHE.Age.cols <- data.frame(age_group=rev(sort(unique(PHE.metadata.linked$age_group))),stringsAsFactors = T)
PHE.Age.cols$age_group.cols <- c(brewer.pal(n=4,"YlGnBu"),"grey75")
# Sample Date colour scheme
PHE.year.cols <- data.frame(year=(sort(unique(PHE.metadata.linked$year))),stringsAsFactors = T)
PHE.year.cols$year.cols <- brewer.pal(n=7,"YlOrRd")
# Sample Date (all global data, but with 1990 cuttoff)
TPA.year.cuttoff.cols <- data.frame(date.cuttoff=c("<1999",1999:2019), date.cuttoff.col=c("#F2F2F2",colorRampPalette(brewer.pal(7, "YlOrRd"))(length(1999:2019))))
##### ## First describe the sequenced population as a whole
Set order of PHE regions
PHE.metadata.linked$phe_centre <- factor(PHE.metadata.linked$phe_centre, levels=rev(PHE.region.cols.brew$UKHSA.region))
Generate some basic statistics about geographical PHE regions (anonymised)
PHE.count.all <- PHE.metadata.linked %>%
dplyr::summarise(count.per.region=n())
PHE.count.years <- PHE.metadata.linked %>%
dplyr::group_by(year) %>%
dplyr::summarise(count.per.year=n()) %>%
ungroup() %>%
dplyr::mutate(perc.per.year=(count.per.year/sum(count.per.year))*100)
# Generate some stats about HIV status
PHE.HIV.counts <- PHE.metadata.linked %>%
dplyr::group_by(hivpos) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::mutate(fraction=Count/total.region) %>%
dplyr::arrange(desc(hivpos), .by_group=T) %>%
dplyr::mutate(cum_fract = cumsum(fraction)) %>%
dplyr::mutate(cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(HIV.perc=(Count/sum(Count)*100))
# Generate some stats about gender orientation
PHE.orientation.counts <- PHE.metadata.linked %>%
dplyr::group_by(gender_orientation) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(gender_orientation), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(orientation.perc=(Count/sum(Count)*100))
# Generate some stats about UK born (vague category that's unfortunately only marginally helpful)
PHE.UKborn.counts <- PHE.metadata.linked %>%
dplyr::group_by(ukborn) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(ukborn), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(UKborn.perc=(Count/sum(Count)*100))
# Generate some stats about London based
PHE.London.counts <- PHE.metadata.linked %>%
dplyr::group_by(london) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(london), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(London.perc=(Count/sum(Count)*100))
# Generate some stats about Age group
PHE.Age.counts <- PHE.metadata.linked %>%
dplyr::group_by(age_group) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(age_group), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(Age.perc=(Count/sum(Count)*100))
# Generate some stats about Lineage group
PHE.Lineage.counts <- PHE.metadata.linked %>%
dplyr::group_by(TPA_Lineage) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(TPA_Lineage), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(Lineage.perc=(Count/sum(Count)*100))
# Generate some stats about sublineage group
PHE.sublineage.counts <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(TPA.pinecone.sublineage), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(Sublineage.perc=(Count/sum(Count)*100))
Make some plots
# Make hbar plot of sample counts by region
p.all.hbarplot <- ggplot(PHE.count.all, aes(x=count.per.region,y="")) +
geom_barh(stat="identity", position="stack", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
scale_fill_manual(values="grey30") +
geom_text(data=PHE.count.all, aes((count.per.region+12), "",label=count.per.region), size=theme.text.size.within, inherit.aes = F) +
labs(y="All", x="Sample Count") +
coord_cartesian(xlim=c(0,260)) +
guides(fill=guide_legend(nrow=4))
#p.all.hbarplot
# make temporal bubbleplot of counts by region
p.all.year.bubbleplot <- ggplot(PHE.count.years, aes(as.numeric(year), y="All")) +
geom_point(alpha=0.65, aes(size=count.per.year)) +
geom_line(alpha=0.25) +
guides(colour='none') +
scale_size_area(max_size = 7,breaks=c(1,5,10,25,50)) +
guides(size=guide_legend(nrow=2)) +
theme_light() +
scale_fill_manual(values="grey30") +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
labs(y="", x="Sample Year", size="Count")
#p.all.year.bubbleplot
# Make proportional hbar plot of HIV status
p.all.hiv.hbarplot <- ggplot(PHE.HIV.counts, aes(Count,y="",fill=hivpos)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
scale_fill_manual(name="HIV +ve",values=PHE.hiv.cols$hiv.cols, breaks=PHE.hiv.cols$hivpos) +
labs(y="All", x="HIV +ve") +
guides(fill=guide_legend(nrow=3)) +
geom_text(data=PHE.HIV.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F) +
NULL
#p.all.hiv.hbarplot
p.all.orientation.hbarplot <- ggplot(PHE.orientation.counts, aes(Count,y="",fill=gender_orientation)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
scale_fill_manual(name="Orientation",values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) +
labs(y="All", x="Orientation") +
guides(fill=guide_legend(nrow=3)) +
geom_text(data=PHE.orientation.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F)
#p.all.orientation.hbarplot
p.all.ukborn.hbarplot <- ggplot(PHE.UKborn.counts, aes(Count,y="",fill=ukborn)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
scale_fill_manual(name="UK\nBorn",values=PHE.ukborn.cols$ukborn.cols, breaks=PHE.ukborn.cols$ukborn) +
labs(y="All", x="UK Born") +
#guides(fill=guide_legend(nrow=3)) +
geom_text(data=PHE.UKborn.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F)
#p.all.ukborn.hbarplot
p.all.London.hbarplot <- ggplot(PHE.London.counts, aes(Count,y="",fill=london)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
scale_fill_manual(name="London",values=PHE.london.cols$london.cols, breaks=PHE.london.cols$london) +
labs(y="All", x="London") +
guides(fill=guide_legend(nrow=3)) +
geom_text(data=PHE.London.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F)
#p.all.London.hbarplot
p.all.Age.hbarplot <- ggplot(PHE.Age.counts, aes(Count,y="",fill=age_group)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
scale_fill_manual(name="Age\nGroup",values=PHE.Age.cols$age_group.cols, breaks=PHE.Age.cols$age_group) +
labs(y="All", x="Age Group") +
guides(fill=guide_legend(nrow=3)) +
geom_text(data=PHE.Age.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F)
#p.all.Age.hbarplot
Plot combined plot for ‘all samples’
PHE.all.combiplot.1 <- plot_grid(p.all.year.bubbleplot, p.all.hbarplot + y.theme.strip, p.all.orientation.hbarplot + y.theme.strip, p.all.hiv.hbarplot + y.theme.strip, p.all.Age.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(4,2,2,2,2), scale=0.9)
PHE.all.combiplot.1
Next just describe population distributions by PHE region
# generate some basic statistics about geographical PHE regions (anonymised)
PHE.geo.count <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre) %>%
dplyr::summarise(count.per.region=n()) %>%
dplyr::mutate(total.count=sum(count.per.region),fraction=count.per.region/total.count)
PHE.geo.count.years <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre,year) %>%
dplyr::summarise(count.per.region.year=n())`summarise()` has grouped output by 'phe_centre'. You can override using the `.groups` argument.PHE.geo.count.years.lineage <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre,year,TPA_Lineage) %>%
dplyr::summarise(count.per.region.year=n()) %>%
dplyr::mutate(total.count.year=sum(count.per.region.year)) %>%
dplyr::ungroup() %>%
tidyr::pivot_wider(names_from=TPA_Lineage, values_from = count.per.region.year)`summarise()` has grouped output by 'phe_centre', 'year'. You can override using the `.groups` argument.PHE.geo.count.years.lineage[is.na(PHE.geo.count.years.lineage)] <- 0
PHE.geo.count.years.lineage$year <- as.numeric(PHE.geo.count.years.lineage$year)
# Generate some stats about HIV status
PHE.geo.HIV.counts <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre,hivpos) %>%
dplyr::summarise(count.per.region.hiv=n()) %>%
dplyr::mutate(total.region=sum(count.per.region.hiv)) %>%
dplyr::mutate(fraction=count.per.region.hiv/total.region) %>%
dplyr::arrange(desc(hivpos), .by_group=T) %>%
dplyr::mutate(cum_fract = cumsum(fraction)) %>%
dplyr::mutate(cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'phe_centre'. You can override using the `.groups` argument.# Double Check HIV status data for non-PHE dataset - confirmed no HIV+ves from non-MSM.
PHE.sourcelab.HIV.counts <- PHE.metadata.linked %>%
dplyr::group_by(is.PHE, gender_orientation, hivpos) %>%
dplyr::summarise(count.per.orientation.hiv=n()) #%>%`summarise()` has grouped output by 'is.PHE', 'gender_orientation'. You can override using the `.groups` argument. #dplyr::filter(is.PHE!="PHE")
# Get total population stats for HIV
PHE.all.HIV.counts <- PHE.metadata.linked %>%
dplyr::group_by(hivpos) %>%
dplyr::summarise(count.hiv=n()) %>%
dplyr::mutate(count.total=sum(count.hiv), fraction=count.hiv/count.total)
# Generate some stats about gender orientation
PHE.orientation.counts <- PHE.metadata.linked %>%
dplyr::group_by(gender_orientation) %>%
dplyr::summarise(orientation.count=n()) %>%
dplyr::mutate(orientation.percent=(orientation.count/sum(orientation.count)*100))
PHE.geo.orientation.counts <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre,gender_orientation) %>%
dplyr::summarise(count.per.region.orientation=n()) %>%
dplyr::mutate(total.region=sum(count.per.region.orientation)) %>%
dplyr::arrange(desc(gender_orientation), .by_group=T) %>%
dplyr::mutate(fraction=count.per.region.orientation/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(orientation.percent=(count.per.region.orientation/sum(count.per.region.orientation)*100))`summarise()` has grouped output by 'phe_centre'. You can override using the `.groups` argument.# Generate some stats about UK born
PHE.geo.UKborn <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre, ukborn) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(ukborn), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'phe_centre'. You can override using the `.groups` argument.
# Generate some stats about London based
PHE.geo.London <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre, london) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(london), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'phe_centre'. You can override using the `.groups` argument.# Generate some stats about Age group
PHE.geo.Age <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre, age_group) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(age_group), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'phe_centre'. You can override using the `.groups` argument.# Generate some stats about Lineage group
PHE.geo.Lineage <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre, TPA_Lineage) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(TPA_Lineage), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'phe_centre'. You can override using the `.groups` argument.# Generate some stats about sublineage group
PHE.geo.sublineage <- PHE.metadata.linked %>%
dplyr::group_by(phe_centre, TPA.pinecone.sublineage) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.region=sum(Count)) %>%
dplyr::arrange(desc(TPA.pinecone.sublineage), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'phe_centre'. You can override using the `.groups` argument.
Make some plots
# Make hbar plot of sample counts by region
p.region.hbarplot <- ggplot(PHE.geo.count, aes(count.per.region,phe_centre, fill=phe_centre)) +
geom_barh(stat="identity", position="stack", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="UKHSA\nRegion",values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region) +
geom_text(data=PHE.geo.count, aes((count.per.region+12), phe_centre,label=count.per.region), size=theme.text.size.within, inherit.aes = F) +
labs(y="UKHSA Region", x="Sample Count") +
#coord_cartesian(xlim=c(0,130)) +
coord_cartesian(xlim=c(0,260)) +
guides(fill=guide_legend(ncol=2))
#p.region.hbarplot
# make temporal bubbleplot of counts by region
p.region.year.bubbleplot <- ggplot(PHE.geo.count.years, aes(as.numeric(year), phe_centre, colour=phe_centre)) +
geom_point(alpha=0.65, aes(size=count.per.region.year)) +
geom_line(alpha=0.25) +
guides(colour='none') +
scale_size_area(max_size = 7,breaks=c(1,5,10,25,50)) +
guides(size=guide_legend(nrow=2, direction = 'horizontal', byrow=T)) +
theme_light() +
scale_color_manual(name="UKHSA\nRegion",values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region) +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
labs(y="UKHSA Region", x="Sample Year", size="Count")
#p.region.year.bubbleplot
# Or a barplot of lineage by year & PHE region?
p.region.year.bubbleplot.barplot.facet.lineage <- PHE.geo.count.years.lineage %>% tidyr::pivot_longer(c(SS14, Nichols), names_to="TPA_Lineage", values_to="Count") %>%
ggplot(aes(year, Count, fill=TPA_Lineage)) +
geom_bar(stat='identity', width=0.6) +
facet_grid(phe_centre~., scales='free') +
guides(size=guide_legend(nrow=2)) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="TPA\nLineage",values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage) +
theme(strip.background = element_rect(color='white', fill='white',linetype="solid"), strip.text.y = element_text(color = "grey25", size=7, angle=0))
#p.region.year.bubbleplot.barplot.facet.lineage
# Make proportional hbar plot of HIV status
p.region.hiv.hbarplot <- ggplot(PHE.geo.HIV.counts, aes(count.per.region.hiv,phe_centre,fill=hivpos)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="HIV +ve",values=PHE.hiv.cols$hiv.cols, breaks=PHE.hiv.cols$hivpos) +
labs(y="UKHSA Region", x="HIV +ve") +
guides(fill=guide_legend(nrow=3)) +
geom_text(data=PHE.geo.HIV.counts, aes(cum_fract.mid, phe_centre,label=count.per.region.hiv), size=theme.text.size.within, inherit.aes = F) +
NULL
#p.region.hiv.hbarplot
p.region.orientation.hbarplot <- ggplot(PHE.geo.orientation.counts, aes(count.per.region.orientation,phe_centre,fill=gender_orientation)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="Orientation",values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) +
labs(y="UKHSA Region", x="Orientation") +
guides(fill=guide_legend(ncol=1)) +
geom_text(data=PHE.geo.orientation.counts, aes(cum_fract.mid, phe_centre,label=count.per.region.orientation), size=theme.text.size.within, inherit.aes = F)
#p.region.orientation.hbarplot
p.region.ukborn.hbarplot <- ggplot(PHE.geo.UKborn, aes(Count,phe_centre,fill=ukborn)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="UK Born",values=PHE.ukborn.cols$ukborn.cols, breaks=PHE.ukborn.cols$ukborn) +
labs(y="UKHSA Region", x="UK Born") +
guides(fill=guide_legend(nrow=3)) +
geom_text(data=PHE.geo.UKborn, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.region.ukborn.hbarplot
p.region.London.hbarplot <- ggplot(PHE.geo.London, aes(Count,phe_centre,fill=london)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="London",values=PHE.london.cols$london.cols, breaks=PHE.london.cols$london) +
labs(y="UKHSA Region", x="London") +
guides(fill=guide_legend(nrow=3)) +
geom_text(data=PHE.geo.London, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.region.London.hbarplot
p.region.Age.hbarplot <- ggplot(PHE.geo.Age, aes(Count,phe_centre,fill=age_group)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="Age\nGroup",values=PHE.Age.cols$age_group.cols, breaks=PHE.Age.cols$age_group) +
labs(y="UKHSA Region", x="Age Group") +
guides(fill=guide_legend(ncol=1)) +
geom_text(data=PHE.geo.Age, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.region.Age.hbarplot
Combined plot
PHE.region.combiplot.1 <- plot_grid(p.region.year.bubbleplot, p.region.hbarplot + y.theme.strip, p.region.orientation.hbarplot + y.theme.strip, p.region.hiv.hbarplot + y.theme.strip, p.region.Age.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(4,2,2,2,2), scale=0.9)
PHE.region.combiplot.1
Regions as a complex multipanel plot
# legends
PHE.region.combiplot.1.legends <- plot_grid(get_legend(p.region.year.bubbleplot), get_legend(p.region.hbarplot + y.theme.strip), get_legend(p.region.orientation.hbarplot + y.theme.strip), get_legend(p.region.hiv.hbarplot + y.theme.strip), get_legend(p.region.Age.hbarplot + y.theme.strip), nrow=1, align="h", rel_widths=c(6,4,4,4,4), scale=0.95)
# Arrange plots vertically
p.year.bubbleplot.combi <- plot_grid(p.all.year.bubbleplot + x.theme.strip, p.region.year.bubbleplot + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
p.region.hbar.counts.combi <- plot_grid(p.all.hbarplot + x.theme.strip + y.theme.strip, p.region.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
p.region.hbar.orientation.combi <- plot_grid(p.all.orientation.hbarplot + x.theme.strip + y.theme.strip, p.region.orientation.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
p.region.hbar.hiv.combi <- plot_grid(p.all.hiv.hbarplot + x.theme.strip + y.theme.strip, p.region.hiv.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
p.region.hbar.Age.combi <- plot_grid(p.all.Age.hbarplot + x.theme.strip + y.theme.strip, p.region.Age.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
# Combine the plots
p.region.hbar.combi.plus.all <- plot_grid(p.year.bubbleplot.combi, p.region.hbar.counts.combi, p.region.hbar.orientation.combi, p.region.hbar.hiv.combi, p.region.hbar.Age.combi, nrow=1, rel_widths=c(6,4,4,4,4), labels = c("A","B","C","D","E"), label_size=panel.lab.size, vjust=0.25)
# and add the legends on top
p.region.hbar.combi.plus.all.with.legends <- plot_grid(p.region.hbar.combi.plus.all, PHE.region.combiplot.1.legends, ncol=1, rel_heights=c(6,1), scale = 0.95)
p.region.hbar.combi.plus.all.with.legends
#ggsave(paste0(Figure_output_directory, "SupFig2_TPA-PHE_Sample-metadistros-by-phe_region+all-combi.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=240, height=135, device='pdf', dpi=1200)
Now lets look at some genetic data
### Make ML tree with sublineage tippoints
TPA.MLtree.ggtree.tippoint <- TPA.MLtree.ggtree %<+% data.frame(Sample_Name=TPA.meta2.1$Sample_Name, Sublineage=TPA.meta2.1$TPA.pinecone.sublineage,stringsAsFactors = F) +
geom_tippoint(aes(color=Sublineage), size=0.5, alpha=0.5, show.legend = FALSE) +
scale_color_manual(name="Sublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage)
Add metadata
# Continent
p.TPA.MLtree.PHE <- gheatmap(TPA.MLtree.ggtree.tippoint,
TPA.rawseq.continents.p, color=NULL,width=0.075,offset=0.00000025, colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) +
scale_fill_manual(name="Continent",values=continental.cols.brew2$continent.col, breaks=continental.cols.brew2$Continent, guide = guide_legend(order = 1,ncol=2)) +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
new_scale_fill()Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# is UK
p.TPA.MLtree.PHE <- gheatmap(p.TPA.MLtree.PHE,
TPA.rawseq.UK.p, color=NULL,width=0.075,offset=0.00001025, colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) +
scale_fill_manual(name="England/Other", values=c("black","grey95"), breaks=c("England","Other"), guide = guide_legend(order = 2,ncol=2)) +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
new_scale_fill()Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# Lineage
p.TPA.MLtree.PHE <- gheatmap(p.TPA.MLtree.PHE,TPA.rawseq.Lineage.p, color=NULL,width=0.075,offset=0.00002025, colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) +
scale_fill_manual(name="Lineage",values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage, guide = guide_legend(order = 3, ncol=2)) + theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
new_scale_fill() +
NULLScale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# sublineage
p.TPA.MLtree.PHE <- gheatmap(p.TPA.MLtree.PHE, data.frame(row.names=TPA.meta2.1$Sample_Name, Sublineage=TPA.meta2.1$TPA.pinecone.sublineage,stringsAsFactors = F), color=NULL,width=0.075,offset=0.00003025, colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) +
scale_fill_manual(name="Sublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage, guide = guide_legend(order = 4, ncol=3)) + theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
new_scale_fill() +
NULLScale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
plot
p.TPA.MLtree.PHE
#ggsave(paste0(Figure_output_directory, "SupFig3_TPA-PHE_Global_Phylo+UK-highlights.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=185, height=160, device='pdf', dpi=1200)
### Geographic distributions of Lineages and Sublineages What about sublineages?
p.region.Lineage.hbarplot <- ggplot(PHE.geo.Lineage, aes(Count,phe_centre,fill=TPA_Lineage)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="TPA\nLineage",values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage) +
labs(y="UKHSA Region", x="TPA Lineage") +
guides(fill=guide_legend(nrow=3)) +
#geom_text(data=PHE.geo.Lineage, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F) +
NULL
p.region.sublineage.hbarplot <- ggplot(PHE.geo.sublineage, aes(Count,phe_centre,fill=TPA.pinecone.sublineage)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="TPA\nSublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage) +
labs(y="UKHSA Region", x="TPA Sublineage") +
guides(fill=guide_legend(nrow=4)) +
#geom_text(data=PHE.geo.sublineage, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F) +
NULL
Combi plot (geography lineages)
PHE.region.combiplot.2.lineages <- plot_grid(p.region.year.bubbleplot +legend.strip, p.region.hbarplot + y.theme.strip + legend.strip + coord_cartesian(xlim=c(0,150)), p.region.Lineage.hbarplot + y.theme.strip +legend.strip, p.region.sublineage.hbarplot + y.theme.strip +legend.strip, nrow=1, align="h", rel_widths=c(6,3,4,4), scale=0.99, labels=c("C","D","E","F"), label_size=panel.lab.size)Coordinate system already present. Adding new coordinate system, which will replace the existing one.# separate out the plot for the legends
p.region.year.bubbleplot.legend <- get_legend(p.region.year.bubbleplot)
p.region.hbarplot.legend <- get_legend(p.region.hbarplot + y.theme.strip)
p.region.Lineage.hbarplot.legend <- get_legend(p.region.Lineage.hbarplot + y.theme.strip)
p.region.sublineage.hbarplot.legend <- get_legend(p.region.sublineage.hbarplot + y.theme.strip)
PHE.region.combiplot.2.lineages.legend <- plot_grid(p.region.year.bubbleplot.legend, p.region.hbarplot.legend, p.region.Lineage.hbarplot.legend, p.region.sublineage.hbarplot.legend, nrow=1, align="h", rel_widths=c(6,3,4,4))
PHE.region.combiplot.2.lineages <- plot_grid(PHE.region.combiplot.2.lineages, PHE.region.combiplot.2.lineages.legend, rel_heights = c(4,1), ncol=1)
PHE.region.combiplot.2.lineages
OK, let’s now add a map of these geographical distributions
Let’s used ONS published shape files - there is one available that shows Public Health England region boundaries.
# Generate approximate regional GPS coords
PHE.region.GPS <- data.frame(
stringsAsFactors = FALSE,
phe_centre = c("East Midlands",
"East of England","London","North East","North West",
"South East","South West","West Midlands",
"Yorkshire and Humber","UK (not England)","Not Known"),
Longitude = c(-0.7,0.5,-0.2,-1.9,-2.4,
0.05,-2.9,-2,-0.8,0.1,0.63),
Latitude = c(52.9,52.4,51.5,55,53.7,
51.1,51,52.6,53.8,54.7,54.1)
)
PHE.region.GPS <- left_join(PHE.region.GPS, PHE.geo.Lineage[PHE.geo.Lineage$TPA_Lineage=="SS14",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS)[4] <- "SS14"
PHE.region.GPS <- left_join(PHE.region.GPS, PHE.geo.Lineage[PHE.geo.Lineage$TPA_Lineage=="Nichols",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS)[5] <- "Nichols"
PHE.region.GPS[is.na(PHE.region.GPS)] <- 0
PHE.region.GPS <- left_join(PHE.region.GPS, PHE.geo.Lineage[PHE.geo.Lineage$TPA_Lineage=="SS14",c("phe_centre","total.region")], by="phe_centre")
colnames(PHE.region.GPS)[6] <- "Region_Count"
PHE.region.GPS$radius <- 0.5*(1-1/sqrt(PHE.region.GPS$Region_Count))
###############################
# Import datafile from https://geoportal.statistics.gov.uk/datasets/public-health-england-centres-december-2016-full-clipped-boundaries-in-england/explore?location=52.950000%2C-2.000000%2C6.88
UK.shapefile <- readOGR(dsn=UK.publichealth.shapefile.data)Warning in OGRSpatialRef(dsn, layer, morphFromESRI = morphFromESRI, dumpSRS = dumpSRS, :
Discarded datum Ordnance_Survey_of_Great_Britain_1936 in Proj4 definition: +proj=tmerc +lat_0=49 +lon_0=-2 +k=0.9996012717 +x_0=400000 +y_0=-100000 +ellps=airy +units=m +no_defsOGR data source with driver: ESRI Shapefile
Source: "/Users/mb29/Papers/Treponema_UK-PHE-gen-epi_2021/Github/Syphilis_Genomic_Epi_England_2022-23/inputdata/Public_Health_England_Centres_(December_2016)_Boundaries", layer: "Public_Health_England_Centres_(December_2016)_Boundaries"
with 9 features
It has 9 fields#Reshape for ggplot2 using the Broom package
UK.mapdata <- tidy(UK.shapefile, region="phec16nm")
#UK.gg <- ggplot() + geom_polygon(data = UK.mapdata, aes(x = long, y = lat, group = group), color = "#FFFFFF", size = 0.25)
UK.gg <- ggplot() + geom_polygon(data = UK.mapdata, aes(x = long, y = lat, group = group), color="grey25", fill="grey90", size = 0.075)
#UK.gg <- UK.gg + coord_fixed(1) + theme_nothing()
#UK.gg
# Map plotting file becomes _very_ big - use ggrastr to reduce the size
UK.gg <-ggplot() + ggrastr::rasterise(geom_polygon(data = UK.mapdata, aes(x = long, y = lat, group = group), color="grey25", fill="grey90", size = 0.075), dpi=400) + coord_fixed(1) + theme_nothing()
#rasterise(geom_point(aes(carat, price, colour = cut), data=diamonds), dpi=30)
# Convert UK regions to be compatible with map
# First find centre point for each region
UK.mapdata.regions.meancoords <- UK.mapdata %>% dplyr::group_by(id) %>%
dplyr::summarise(mean.lat=mean(lat), mean.long=median(long)) %>%
dplyr::ungroup()
colnames(UK.mapdata.regions.meancoords)[1] <- "phe_centre"
PHE.region.GPS.ukmap <- dplyr::left_join(PHE.region.GPS, UK.mapdata.regions.meancoords, by="phe_centre")
# Add artificial location for 'not known'
PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre=="Not Known","mean.lat"] <- 600000
PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre=="Not Known","mean.long"] <- 550000
# Shift "South East" slightly to reduce the overlap with London
PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre=="South East","mean.long"] <- 475000
# Shift "East of England East" slightly to reduce the overlap with London
PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre=="East of England","mean.lat"] <- 275000
# Not going to try plotting the 2 samples from elsewhere in the UK, so remove that row
PHE.region.GPS.ukmap <- PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre != "UK (not England)",]
# Create radius variable for plotting pie sizes (use log10(n)*20,000)
PHE.region.GPS.ukmap$radius.UK <- log10(PHE.region.GPS.ukmap$Region_Count)*20000
#PHE.geo.count.years.lineage
UK.gg.scatterpie <- UK.gg + geom_scatterpie(data=PHE.region.GPS.ukmap, aes(mean.long, mean.lat, group=phe_centre, r=radius.UK), alpha=0.85, color=NA, cols=c("Nichols","SS14")) +
scale_fill_manual(name="TPA\nLineage",values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage) + theme(legend.position="top")
UK.gg.scatterpie <- UK.gg.scatterpie + geom_scatterpie_legend(PHE.region.GPS.ukmap[!is.na(PHE.region.GPS.ukmap$mean.lat),"radius.UK"], labeller=function(x) round((10^(x/20000)),0), n=3, x=150000, y=500000)
UK.gg.scatterpie <- UK.gg.scatterpie + theme_nothing()
#? Add labels
UK.gg.scatterpie.labs <- UK.gg.scatterpie + geom_label_repel(data=PHE.region.GPS.ukmap[!is.na(PHE.region.GPS.ukmap$mean.lat),], aes(mean.long, mean.lat, label=phe_centre), size=theme.text.size.within, nudge_x = 50000, nudge_y = -25000, segment.size = 0.1) + theme(legend.key.size = unit(0.55,"line"), legend.position="bottom") +
theme.text.size +
theme_nothing()
UK.gg.scatterpie.labs
Now do an equivalent plot for sublineages
PHE.region.GPS.ukmap.sublin <- PHE.region.GPS.ukmap
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="1",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[11] <- "1"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="2",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[12] <- "2"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="3",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[13] <- "3"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="6",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[14] <- "6"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="8",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[15] <- "8"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="14",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[16] <- "14"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="15",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[17] <- "15"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="16",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[18] <- "16"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="Singleton",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[19] <- "Singleton"
PHE.region.GPS.ukmap.sublin[is.na(PHE.region.GPS.ukmap.sublin)] <- 0
# Most samples are either sublineage 1 or 14. Let's create a count of samples that are neither.
PHE.region.GPS.ukmap.sublin$`Other Sublineages` <- sapply(1:nrow(PHE.region.GPS.ukmap.sublin), function (x) PHE.region.GPS.ukmap.sublin$Region_Count[x]-sum(PHE.region.GPS.ukmap.sublin$`1`[x], PHE.region.GPS.ukmap.sublin$`14`[x]))
UK.gg.scatterpie.sublineage <- UK.gg + geom_scatterpie(data=PHE.region.GPS.ukmap.sublin[PHE.region.GPS.ukmap.sublin$mean.long!=0,], aes(mean.long, mean.lat, group=phe_centre, r=radius.UK), alpha=0.85, color=NA, cols=c("1","14","Other Sublineages")) +
scale_fill_manual(name="TPA\nSublineage",values=c("#FC9272","#BCBDDC", "grey50"), breaks=c("1","14","Other Sublineages"))
# add legend
UK.gg.scatterpie.sublineage <- UK.gg.scatterpie.sublineage + geom_scatterpie_legend(PHE.region.GPS.ukmap[!is.na(PHE.region.GPS.ukmap$mean.lat),"radius.UK"], labeller=function(x) round((10^(x/20000)),0), n=3, x=150000, y=500000)
#UK.gg.scatterpie <- UK.gg.scatterpie + x.theme.strip + y.theme.strip
UK.gg.scatterpie.sublineage <- UK.gg.scatterpie.sublineage + theme_nothing()
#? Add labels
UK.gg.scatterpie.sublineage <- UK.gg.scatterpie.sublineage + geom_label_repel(data=PHE.region.GPS.ukmap[!is.na(PHE.region.GPS.ukmap$mean.lat),], aes(mean.long, mean.lat, label=phe_centre), size=theme.text.size.within, nudge_x = 50000, nudge_y = -25000, segment.size = 0.1) +
theme(legend.key.size = unit(0.55,"line"), legend.position="bottom") +
theme.text.size +
theme_nothing()
UK.gg.scatterpie.sublineage
Combined map plot
UK.gg.scatterpie.combi <- plot_grid(UK.gg.scatterpie.labs, UK.gg.scatterpie.sublineage, ncol=2, labels = c("A","B"), label_size=panel.lab.size)
UK.gg.scatterpie.combi
Plot in combination with barplots
plot_grid(UK.gg.scatterpie.combi, PHE.region.combiplot.2.lineages, nrow=2, rel_heights=c(4,5))
#ggsave(paste0(Figure_output_directory,"Fig2_TPA-PHE_Map-Lineage+Barplots.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=190, height=185, device='pdf', dpi=1200)
### Analysis by sublineage
Now lets start exploring how samples are distributed by sublineage
PHE.metadata.linked <- PHE.metadata.linked
PHE.metadata.linked$TPA.pinecone.sublineage <- factor(PHE.metadata.linked$TPA.pinecone.sublineage, levels=rev(as.character(sort(unique(PHE.metadata.linked$TPA.pinecone.sublineage)))))
PHE.Lineage.count <- PHE.metadata.linked %>%
dplyr::group_by(TPA_Lineage) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total=sum(Count), perc=(Count/total)*100)
PHE.sublin.count <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total=sum(Count), perc=(Count/total)*100)
PHE.geo.sublin.years <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage,year) %>%
dplyr::summarise(Count=n())`summarise()` has grouped output by 'TPA.pinecone.sublineage'. You can override using the `.groups` argument.## Generate some stats about sublineage groups
# Generate some stats about gender orientation
PHE.sublineage.orientation.counts <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage,gender_orientation) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.sublin=sum(Count)) %>%
dplyr::arrange(desc(gender_orientation), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'TPA.pinecone.sublineage'. You can override using the `.groups` argument.# Generate some stats about UK born
PHE.sublineage.UKborn <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage, ukborn) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.sublin=sum(Count)) %>%
#dplyr::arrange(desc(ukborn), .by_group=T) %>%
dplyr::arrange(desc(ukborn), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'TPA.pinecone.sublineage'. You can override using the `.groups` argument.
# Generate some stats about London based
PHE.sublineage.London <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage, london) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.sublin=sum(Count)) %>%
dplyr::arrange(desc(london), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'TPA.pinecone.sublineage'. You can override using the `.groups` argument.# Generate some stats about Age group
PHE.sublineage.Age <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage, age_group) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.sublin=sum(Count)) %>%
dplyr::arrange(desc(age_group), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'TPA.pinecone.sublineage'. You can override using the `.groups` argument.# Generate some stats about HIV group
PHE.sublineage.HIV <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage, hivpos) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.sublin=sum(Count)) %>%
dplyr::arrange(desc(hivpos), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'TPA.pinecone.sublineage'. You can override using the `.groups` argument.# Generate some stats by PHE Region
PHE.sublineage.PHEcentre <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage, phe_centre) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.sublin=sum(Count)) %>%
dplyr::arrange(desc(phe_centre), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'TPA.pinecone.sublineage'. You can override using the `.groups` argument.
Plot by sublineage
p.sublineage.year.bubbleplot <- ggplot(PHE.geo.sublin.years, aes(as.numeric(year), TPA.pinecone.sublineage, colour=TPA.pinecone.sublineage)) +
geom_point(alpha=0.65, aes(size=Count)) +
geom_line(alpha=0.25) +
guides(colour='none') +
scale_size_area(max_size = 7,breaks=c(1,5,10,25,50)) +
guides(size=guide_legend(nrow=2, direction = 'horizontal', byrow=T)) +
theme_light() +
scale_color_manual(name="TPA\nSublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage) +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
labs(y="TPA Sublineage", x="Sample Year", size="Count")
#p.sublineage.year.bubbleplot
p.sublineage.hbarplot <- ggplot(PHE.sublin.count, aes(Count,TPA.pinecone.sublineage,fill=TPA.pinecone.sublineage)) +
geom_barh(stat="identity", position="stack", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="TPA\nSublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage) +
labs(y="TPA Sublineage", x="Sample Count") +
geom_text(data=PHE.sublin.count, aes((Count+12), TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F) +
#coord_cartesian(xlim=c(0,200)) +
coord_cartesian(xlim=c(0,260)) +
guides(fill=guide_legend(ncol=2))
#p.sublineage.hbarplot
p.sublineage.orientation.hbarplot <- ggplot(PHE.sublineage.orientation.counts, aes(y=TPA.pinecone.sublineage,x=Count,fill=gender_orientation)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="Orientation",values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) +
labs(y="TPA Sublineage", x="Orientation") +
guides(fill=guide_legend(ncol=1)) +
geom_text(data=PHE.sublineage.orientation.counts, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.region.orientation.hbarplot
p.sublineage.hiv.hbarplot <- ggplot(PHE.sublineage.HIV, aes(y=TPA.pinecone.sublineage, x=Count,fill=hivpos)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="HIV +ve",values=PHE.hiv.cols$hiv.cols, breaks=PHE.hiv.cols$hivpos) +
labs(y="TPA Sublineage", x="HIV +ve") +
guides(fill=guide_legend(ncol=1)) +
geom_text(data=PHE.sublineage.HIV, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.sublineage.hiv.hbarplot
p.sublineage.ukborn.hbarplot <- ggplot(PHE.sublineage.UKborn, aes(y=TPA.pinecone.sublineage,x=Count,fill=ukborn)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="UK\nborn",values=PHE.ukborn.cols$ukborn.cols, breaks=PHE.ukborn.cols$ukborn) +
labs(y="TPA Sublineage", x="UK born") +
guides(fill=guide_legend(nrow=3)) +
geom_text(data=PHE.sublineage.UKborn, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.sublineage.ukborn.hbarplot
p.sublineage.Age.hbarplot <- ggplot(PHE.sublineage.Age, aes(y=TPA.pinecone.sublineage, x=Count ,fill=age_group)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="Age\nGroup",values=PHE.Age.cols$age_group.cols, breaks=PHE.Age.cols$age_group) +
labs(y="TPA Sublineage", x="Age Group") +
guides(fill=guide_legend(ncol=1)) +
geom_text(data=PHE.sublineage.Age, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.sublineage.Age.hbarplot
p.sublineage.PHEregion.hbarplot <- ggplot(PHE.sublineage.PHEcentre, aes(y=TPA.pinecone.sublineage, x=Count, fill=phe_centre)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
scale_fill_manual(name="UKHSA\nRegion",values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$PHE.region) +
labs(y="TPA Sublineage", x="UKHSA Region") +
guides(fill=guide_legend(nrow=4)) +
geom_text(data=PHE.sublineage.PHEcentre, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)
Look at how sublineages are distributed by region (sublineage-centric)
p.sublineage.PHEregion.hbarplot
Combine patient metadata into a plot
#PHE.sublineages.combiplot.1 <- plot_grid(p.sublineage.year.bubbleplot, p.sublineage.hbarplot + y.theme.strip, p.sublineage.orientation.hbarplot + y.theme.strip, p.sublineage.hiv.hbarplot + y.theme.strip, p.sublineage.PHEregion.hbarplot + y.theme.strip, p.sublineage.ukborn.hbarplot + y.theme.strip, p.sublineage.Age.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(3,2,2,2,2,2,2), scale=0.9)
#PHE.sublineages.combiplot.1 <- plot_grid(p.sublineage.year.bubbleplot, p.sublineage.hbarplot + y.theme.strip, p.sublineage.orientation.hbarplot + y.theme.strip, p.sublineage.hiv.hbarplot + y.theme.strip, p.sublineage.Age.hbarplot + y.theme.strip, p.sublineage.PHEregion.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(3,2,2,2,2,4), scale=0.9)
PHE.sublineages.combiplot.1 <- plot_grid(p.sublineage.year.bubbleplot, p.sublineage.hbarplot + y.theme.strip, p.sublineage.orientation.hbarplot + y.theme.strip, p.sublineage.hiv.hbarplot + y.theme.strip, p.sublineage.Age.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(4,2,2,2,2), scale=0.9)
PHE.sublineages.combiplot.1
Lets add the ‘all’ row again to the ‘by sublineage’ plot
# legends
PHE.sublineage.combiplot.1.legends <- plot_grid(get_legend(p.sublineage.year.bubbleplot), get_legend(p.sublineage.hbarplot + y.theme.strip), get_legend(p.sublineage.orientation.hbarplot + y.theme.strip), get_legend(p.sublineage.hiv.hbarplot + y.theme.strip), get_legend(p.sublineage.Age.hbarplot + y.theme.strip), nrow=1, align="h", rel_widths=c(6,4,4,4,4), scale=0.95)
# regions
#PHE.sublineage.combiplot.1.nolegend <- plot_grid(p.sublineage.year.bubbleplot + legend.strip, p.sublineage.hbarplot + y.theme.strip + legend.strip, p.sublineage.orientation.hbarplot + y.theme.strip + legend.strip, p.sublineage.hiv.hbarplot + y.theme.strip + legend.strip, p.sublineage.Age.hbarplot + y.theme.strip + legend.strip, nrow=1, align="h", rel_widths=c(4,2,2,2,2), scale=0.9)
# Or do it vertically
p.sublineage.year.bubbleplot.combi <- plot_grid(p.all.year.bubbleplot + x.theme.strip, p.sublineage.year.bubbleplot + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
p.sublineage.hbar.counts.combi <- plot_grid(p.all.hbarplot + x.theme.strip + y.theme.strip, p.sublineage.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
p.sublineage.hbar.orientation.combi <- plot_grid(p.all.orientation.hbarplot + x.theme.strip + y.theme.strip, p.sublineage.orientation.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
p.sublineage.hbar.hiv.combi <- plot_grid(p.all.hiv.hbarplot + x.theme.strip + y.theme.strip, p.sublineage.hiv.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
p.sublineage.hbar.Age.combi <- plot_grid(p.all.Age.hbarplot + x.theme.strip + y.theme.strip, p.sublineage.Age.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))
# Combine the plots
p.sublineage.hbar.combi.plus.all <- plot_grid(p.sublineage.year.bubbleplot.combi, p.sublineage.hbar.counts.combi, p.sublineage.hbar.orientation.combi, p.sublineage.hbar.hiv.combi, p.sublineage.hbar.Age.combi, nrow=1, rel_widths=c(7,3,4,4,4), labels=c("A", "B", "C", "D", "E"),label_size=panel.lab.size, vjust=1, scale=0.99)
# and add the legends on top
#p.sublineage.hbar.combi.plus.all.with.legends <- plot_grid(PHE.sublineage.combiplot.1.legends, p.sublineage.hbar.combi.plus.all, ncol=1, rel_heights=c(1,9))
# legends below
p.sublineage.hbar.combi.plus.all.with.legends <- plot_grid(p.sublineage.hbar.combi.plus.all, PHE.sublineage.combiplot.1.legends, ncol=1, rel_heights=c(8,1))
p.sublineage.hbar.combi.plus.all.with.legends
These patterns look fairly similar between sublineages, and (apart from 1 & 14) the groups are very small. However, sublineage 14 does appear to have a higher proportion of MSM compared to sublineage 1 and others. Let’s test that formally using 2x2 fisher’s tests
PHE.MSM.counts.all <- PHE.metadata.linked %>%
dplyr::group_by(is.MSM, .drop=F) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.sublin=sum(Count)) %>%
dplyr::arrange((is.MSM), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))
PHE.sublineage.MSM.counts <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage,is.MSM, .drop=F) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.sublin=sum(Count)) %>%
dplyr::arrange((is.MSM), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) #%>%`summarise()` has grouped output by 'TPA.pinecone.sublineage'. You can override using the `.groups` argument. #dplyr::filter(!is.na(is.MSM))
PHE.sublineage.MSM.counts.wider <- PHE.sublineage.MSM.counts %>% dplyr::select(TPA.pinecone.sublineage, is.MSM, Count) %>%
tidyr::pivot_wider(names_from = is.MSM, values_from=Count) %>%
dplyr::mutate(MSM=replace_na(MSM, 0), Other=replace_na(Other, 0), Total=sum(MSM,Other)) %>%
#dplyr::select(-`NA`) %>%
dplyr::filter(Total!=0)
PHE.sublineage.MSM.pval <- data.frame(TPA.pinecone.sublineage=PHE.sublineage.MSM.counts.wider$TPA.pinecone.sublineage, p.fisher=sapply(1:nrow(PHE.sublineage.MSM.counts.wider), function (x) fisher.test(matrix(as.numeric(c(PHE.sublineage.MSM.counts.wider[x,"MSM"],
PHE.sublineage.MSM.counts.wider[x,"Other"],
PHE.MSM.counts.all[PHE.MSM.counts.all$is.MSM=="MSM","Count"], PHE.MSM.counts.all[PHE.MSM.counts.all$is.MSM=="Other","Count"])),nrow=2))[[1]]), stringsAsFactors=F)
PHE.sublineage.MSM.counts.wider <- dplyr::left_join(PHE.sublineage.MSM.counts.wider, PHE.sublineage.MSM.pval, by="TPA.pinecone.sublineage")
PHE.sublineage.MSM.counts.wider
### Visualisation of UK genomic relationships
Ok, let’s make a tree for displaying these relationships using the UK dataset only
From some experimentation, a ‘GrapeTree’ minimum spanning network works well for visualising the clonality of these populations. We can use a SNP-scaled phylogeny as direct input to GrapeTree, and this will allow branches to be scaled appropriately. However, although annotation is allowed within the GrapeTree software, colours must be manually edited. Final GrapeTree plots can then be imported back into R for combining with other plots.
Alternative visualisations - grapetree?
Take the 526-global phylogeny (snp-scaled version from pyjar), and prune to only include the UK strains from this study - this ensures the topology is consistent accross studies.
TPA.pyjar.tree.subset.uk <- ape::keep.tip(TPA.pyjar.tree, as.character(unlist(PHE.metadata.linked[PHE.metadata.linked$Geo_Country=="UK","Sample_Name"])))
TPA.pyjar.tree.subset.global_beast_only.seqlanes <- TPA.meta2.1 %>% filter(full.temporal.analysis=='Yes') %>%
select(Cleaned_fastq_id) %>% pull()
TPA.pyjar.tree.subset.uk.seqlanes <- as.character(unlist(PHE.metadata.linked[PHE.metadata.linked$Geo_Country=="UK","Cleaned_fastq_id"]))
ggtree(TPA.pyjar.tree.subset.uk)
#write.tree(TPA.pyjar.tree.subset.uk, paste0(Data_input_directory,"TPA.UK-only.pyjar.2022-02-03.tre"))
# Write out a metadata sheet for the relevant information
PHE.metadata.linked.grapetree <- PHE.metadata.linked[,c("Sample_Name", "year","gender_orientation","phe_centre","hivpos","ukborn","TPA_Lineage","TPA.pinecone.sublineage")]
colnames(PHE.metadata.linked.grapetree)[1] <- "ID"
#write.table(PHE.metadata.linked.grapetree, paste0(Data_input_directory,"TPA.UK-only.grapetree.meta.2022-02-03.tsv"), sep = "\t", quote=F, row.names = F)
Tree independently visualised and annotated using GrapeTree.
Now import and integrate GrapeTree plot with metadata plots.
# Combine the plots
p.sublineage.hbar.combi.plus.all.B2F <- plot_grid(p.sublineage.year.bubbleplot.combi, p.sublineage.hbar.counts.combi, p.sublineage.hbar.orientation.combi, p.sublineage.hbar.hiv.combi, p.sublineage.hbar.Age.combi, nrow=1, rel_widths=c(7,4,4,4,4), labels=c("B", "C", "D", "E", "F"),label_size=panel.lab.size, vjust=1, scale=0.97)
# legends below
p.sublineage.hbar.combi.plus.all.with.legends.B2F <- plot_grid(p.sublineage.hbar.combi.plus.all.B2F, PHE.sublineage.combiplot.1.legends, ncol=1, rel_heights=c(7,1))
#p.sublineage.hbar.combi.plus.all.with.legends.B2F
# Now bring in externally plotted Grapetree
p.TPA.UK.Grapetree.sublineages <- ggdraw() + draw_image(TPA.UK.Grapetree.sublineages.file)
p.TPA.UK.Grapetree.sublineages
p.sublineage.hbar.combi.plus.all.with.legends.B2F.with.grapetree <- plot_grid(p.TPA.UK.Grapetree.sublineages, p.sublineage.hbar.combi.plus.all.with.legends.B2F, ncol=1, labels=c("A",""), label_size=panel.lab.size, rel_heights=c(3,5))
p.sublineage.hbar.combi.plus.all.with.legends.B2F.with.grapetree
#ggsave(paste0(Figure_output_directory, "Fig1_TPA-PHE_Sample-distros-sublineage.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=190, height=185, device='pdf', dpi=1200)
Manage other GrapeTree plots (for consistency)
TPA-UK-2022-02-16.-MSTree_3-way-figure.Inscaped-2
# Bring in 3-way graphetree plot (3 different metadata variables using the same input tree)
TPA.UK.Grapetree.3way <- ggdraw() + draw_image(TPA.UK.Grapetree.3way.file)
TPA.UK.Grapetree.3way
#ggsave(paste0(Figure_output_directory, "SupFig4_TPA-PHE_Grapetree-3ways.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=145, height=180, device='pdf', dpi=1200)
And also do the HIV status plot
TPA.UK.Grapetree.HIV <- ggdraw() + draw_image(TPA.UK.Grapetree.HIV.file)
TPA.UK.Grapetree.HIV
#ggsave(paste0(Figure_output_directory, "SupFig5_TPA-PHE_Grapetree-HIV.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=185, height=110, device='pdf', dpi=1200)
### Phylogenetic context analyses
Ok, now lets look at some trees
First, let’s formalise BEAST tree plotting as three separate functions to enable other trees to be plotted the same way
full.beast2.tree <- read.beast(full.beast2.tree.file)
full.beast2.tree@phylo$tip.label <- gsub("\\|.+$","",full.beast2.tree@phylo$tip.label, perl=T)
################################################################################################
# function to extract a tree based on sublineage
Extract_sublineage_tree_for_plot <- function(my.beast.tree, my.metadata, my.phe.meta, my.sublineage){
# get all tips to include from metadata, then calculate MRCA from tree
sublineage.test.mrca <- getMRCA(my.beast.tree@phylo, as.character(unlist(my.metadata[my.metadata$TPA.pinecone.sublineage==my.sublineage,"Sample_Name"])))
######
TPA.beast.subtree.test <- tree_subset(my.beast.tree, node=sublineage.test.mrca, levels_back=0)
return(TPA.beast.subtree.test)
}
#Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 1)
################################################################################################
################################################################################################
# Function to prepare a beast tree with timescale indicators, posterior support and 95% HPD bars
plot_beast_subtree_with_HPD <- function(my.beast.tree, my.metadata, my.phe.meta, mrsd.fulltree){
# get MRCD for tree
mrsd.Beast.tree.test.s <- max(as.numeric(unlist(my.metadata[my.metadata$Sample_Name %in% my.beast.tree@phylo$tip.label,"Sample_Year"])))
mrsd.Beast.tree.test <- lubridate::ymd(paste0(mrsd.Beast.tree.test.s,"-06-01"))
mrsd.Beast.tree.fulltree <- lubridate::ymd(mrsd.fulltree)
#mrsd.Beast.tree.test
# plot basic tree
options(ignore.negative.edge=TRUE)
p.TPA.beast.subtree.test <- ggtree(my.beast.tree, mrsd=mrsd.Beast.tree.test, ladderize = T, size=0.4) + scale_x_continuous(breaks=seq(1960,2020,10), minor_breaks=seq(2000, 2020, 1)) +
theme_tree2() +
# Add date lines for easy interpretation
theme(panel.grid.major = element_line(color="grey50", size=.2),
panel.grid.minor = element_line(color="grey85", size=.2),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank())
# Add posterior support as node points
p.TPA.beast.subtree.test <- p.TPA.beast.subtree.test + geom_point2(aes(subset=(!isTip & as.numeric(posterior)>0.8)),color="gray60",size=2,alpha=0.5, shape=18) +
geom_point2(aes(subset=(!isTip & as.numeric(posterior)>0.91)),color="gray40",size=3,shape=18,alpha=0.5) +
geom_point2(aes(subset=(!isTip & as.numeric(posterior)>=0.96)),color="black",size=3,shape=18,alpha=0.5)
######
# extract 95% HPD intervals - geom_range seems unable to do correctly with this tree (known bug for tip dated trees), so extract data and plot using geom_segment
TPA.beast.subtree.test.data <- fortify(my.beast.tree)
minmax <- t(matrix(unlist(TPA.beast.subtree.test.data[!is.na(TPA.beast.subtree.test.data$height_0.95_HPD),"height_0.95_HPD"]),nrow=2))
bar_df <- data.frame(node_id=TPA.beast.subtree.test.data[!is.na(TPA.beast.subtree.test.data$height_0.95_HPD),"node"],as.data.frame(minmax))
names(bar_df) <- c('node_id','min','max')
bar_df <- bar_df %>% filter(node_id > Ntip(my.beast.tree@phylo))
bar_df <- bar_df %>% left_join(TPA.beast.subtree.test.data, by=c('node_id'='node')) #%>% select(node_id,min,max,y)
#mrcd.decimal <- decimal_date(mrsd.Beast.tree.test)
mrcd.decimal <- decimal_date(mrsd.Beast.tree.fulltree)
# Now add HPDs to plot
p.TPA.beast.subtree.test <- p.TPA.beast.subtree.test + geom_segment(aes(x=mrcd.decimal-max, y=y, xend=mrcd.decimal-min, yend=y), data=bar_df, color='red', alpha=0.2, size=2.0)
# Output tree
return(p.TPA.beast.subtree.test)
}
################################################################################################
################################################################################################
# Function to add metadata to tree
# Has two optional arguments "initial.track.offset" and "track.scaling" which can be used to alter the width and positioning of metadata tracks
plot_beast_subtree_with_PHE_metadata <- function(my.beast.tree.input, my.metadata, my.phe.meta, initial.track.offset, track.scaling){
# Add code to allow scaling up of the track offsets and widths - useful for much bigger length trees
if(missing(initial.track.offset)){
initial.track.offset <- 0
}
if(missing(track.scaling)){
track.scaling <- 1
}
# Calculate amount to offset each heatmap track
offset.dist <- 4*track.scaling
track.width <- (1/max(my.beast.tree.input$data$height)*3)*track.scaling
# make a list of taxa used in this plot
my.taxa.list <- as.character(unlist(filter(my.beast.tree.input$data, isTip==TRUE) %>% select(label)))
# make a color scale for sampling years
#PHE.sublintest.year.cols <- data.frame(year=sort(unique(as.numeric(unlist(my.metadata[(my.metadata$Sample_Name %in% my.taxa.list),"Sample_Year"],use.names=F)))),stringsAsFactors = T)
#PHE.sublintest.year.cols$year.cols <- colorRampPalette(brewer.pal(7, "YlOrRd"))(nrow(PHE.sublintest.year.cols))
# Or alternatively, use a common colour scheme for all data (maybe more sensible)
PHE.sublintest.year.cols <- data.frame(year=TPA.year.cuttoff.cols$date.cuttoff, year.cols=TPA.year.cuttoff.cols$date.cuttoff.col, stringsAsFactors = F)
# make metadata file for UK regions present in sublineage
sublin.test.region.meta <- data.frame(row.names=as.character(unlist(my.phe.meta[my.phe.meta$Sample_Name %in% my.taxa.list,"Sample_Name"])), Region=as.character(unlist(my.phe.meta[my.phe.meta$Sample_Name %in% my.taxa.list,"phe_centre"])), stringsAsFactors = F)
# Add heatmap strips
# Sample Year
#TPA.beast.subtree.test.global.plot1.regional <- gheatmap(my.beast.tree.input, TPA.rawseq.all.Years.p, color=NULL,width=track.width, offset=initial.track.offset+offset.dist,colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
#scale_fill_manual(name="Year", values=PHE.sublintest.year.cols$year.cols,breaks=PHE.sublintest.year.cols$year, guide = guide_legend(order = 1, ncol=2)) +
#ggnewscale::new_scale_fill()
TPA.beast.subtree.test.global.plot1.regional <- gheatmap(my.beast.tree.input, TPA.rawseq.year.cuttoff.p, color=NULL,width=track.width, offset=initial.track.offset+offset.dist,colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
scale_fill_manual(name="Year", values=PHE.sublintest.year.cols$year.cols,breaks=PHE.sublintest.year.cols$year, guide = guide_legend(order = 1, ncol=2)) +
ggnewscale::new_scale_fill()
# Add country
TPA.beast.subtree.test.global.plot1.regional <- gheatmap(TPA.beast.subtree.test.global.plot1.regional, TPA.rawseq.countries.p, color=NULL,width=track.width, offset=initial.track.offset+(offset.dist*2),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
scale_fill_manual(name="Country", values=continental.country.cols.brew2$country.col, breaks=continental.country.cols.brew2$Geo_Country, guide = guide_legend(order = 2)) +
ggnewscale::new_scale_fill()
# UK or non-UK
TPA.beast.subtree.test.global.plot1.regional <- gheatmap(TPA.beast.subtree.test.global.plot1.regional,
TPA.rawseq.UK.p, color=NULL,width=track.width,offset=initial.track.offset+(offset.dist*3), colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) +
scale_fill_manual(name="England/Other", breaks=c("England","Other"), values=c("black","grey95"), na.value = "white", guide = guide_legend(order = 3, ncol=2)) +
ggnewscale::new_scale_fill()
# UK PHE region
TPA.beast.subtree.test.global.plot1.regional <- gheatmap(TPA.beast.subtree.test.global.plot1.regional, sublin.test.region.meta, color=NULL,width=track.width, offset=initial.track.offset+(offset.dist*4),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
scale_fill_manual(name="UKHSA Region", values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region, na.value = "white", guide = guide_legend(order = 4)) +
ggnewscale::new_scale_fill()
# TPA sublineage
#TPA.beast.subtree.test.global.plot1.regional <- gheatmap(TPA.beast.subtree.test.global.plot1.regional, data.frame(row.names=TPA.meta2.1$Sample_Name, Sublineage=TPA.meta2.1$TPA.pinecone.sublineage, stringsAsFactors = F), color=NULL,width=track.width,offset=initial.track.offset+(offset.dist*5), colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=2.5) +
#scale_fill_manual(name="Sublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage, guide = guide_legend(order = 5))
TPA.beast.subtree.test.global.plot1.regional <- TPA.beast.subtree.test.global.plot1.regional + theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
new_scale_fill() +
geom_rootedge(2) +
NULL
# calculate number of taxa
test.taxacount <- length(my.taxa.list)
# Adjust final plot x and y axis to make space for labels using taxa counts
x.axis.limits <- ggplot_build(TPA.beast.subtree.test.global.plot1.regional)$layout$panel_scales_x[[1]]$range$range
TPA.beast.subtree.test.global.plot1.regional <- TPA.beast.subtree.test.global.plot1.regional +
coord_cartesian(y=c(-0.5-(test.taxacount/15),test.taxacount+2), x=c(x.axis.limits[1],x.axis.limits[2]+3))
return(TPA.beast.subtree.test.global.plot1.regional)
}
################################################################################################
Great, now let’s plot a full beast tree
# function for x-axis time breaks needs tweaking for the full tree
TPA.Global.full.BeastTree.ukmeta <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(my.beast.tree = full.beast2.tree, my.metadata = TPA.meta2.1, my.phe.meta = PHE.metadata.linked, mrsd.fulltree = "2019-06-01") + scale_x_continuous(breaks=seq(1400,2020,50), minor_breaks=seq(1950, 2020, 5)), my.metadata = TPA.meta2.1, my.phe.meta = PHE.metadata.linked, track.scaling = 5)Scale for 'x' is already present. Adding another scale for 'x', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.TPA.Global.full.BeastTree.ukmeta
#ggsave(paste0(Figure_output_directory,"SupFig7_TPA_FullBeastTree.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=185, height=240, device='pdf', dpi=1200)
Now do sublineage plots
Make some plots
# Sublineage 1
sublineage.1.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 1), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, track.scaling = 1.2)Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# Sublineage.2
sublineage.2.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 2), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, track.scaling = 1)Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# Sublineage.8
sublineage.8.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 8), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, track.scaling = 1.1)Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# Sublineage.14
sublineage.14.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 14), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, track.scaling = 1.1)Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Plot together?
Maybe with sublineage 1 expanded?
p.beast.trees.heatmap.sublineages.combi.offset1 <- plot_grid(sublineage.2.tree.heatmap,
sublineage.8.tree.heatmap,
sublineage.14.tree.heatmap,
ncol=2, labels=c("B - Sublineage 2","C - Sublineage 8","D - Sublineage 14"), label_size=panel.lab.size, scale=0.95, vjust=1.0)
p.beast.trees.heatmap.sublineages.combi.offset2 <- plot_grid(sublineage.1.tree.heatmap, p.beast.trees.heatmap.sublineages.combi.offset1, labels=c("A - Sublineage 1", ""), label_size=panel.lab.size, scale=0.975, ncol=2, rel_widths=c(6,11), vjust=2.5)
p.beast.trees.heatmap.sublineages.combi.offset2
#ggsave(paste0(Figure_output_directory,"SupFig8_TPA-PHE_Sublineage-BeastTrees.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=265, height=230, device='pdf', dpi=1200)
Need to explore sublineage 14 a bit more to get dates for those subclades
sublineage.14.tree.heatmap + geom_tiplab(size=theme.text.size.within, linesize=0.4) #3
# Ok, there are multiple subclades in this tree
sublineage.14.tree.heatmap.data <- sublineage.14.tree.heatmap$data
# getMRCA(full.beast2.tree@phylo,c("PHE150150A","NL14","TPA_BCC122","TPA_BCC126","PHE140076A","TPA_UKBRG008")) 982
# full.beast2.tree@phylo$tip.label[phangorn::Descendants(full.beast2.tree@phylo, 982, type = c("tips"))[[1]]]
sublineage.14.lowerclade.list <- c("NL17", "NL19", "PHE140085A", "PHE140089A", "PHE150118A", "PHE150121A", "PHE150133A", "PHE150143A", "PHE150145A", "PHE150162A", "PHE150166A", "PHE150168A", "PHE160224A", "PHE160243A", "PHE160255A", "PHE160276A", "PHE160290A", "PHE160302A", "PHE160306A", "PHE170333A", "PHE170349A", "PHE170374A", "PHE170381A", "PHE170664A", "TPA_ESBCN005", "TPA_UKBIR032")
sublineage.14.upperclade.list <- c("NL14", "PHE140076A", "PHE150149A", "PHE150150A", "PHE150170A", "PHE160196A", "PHE160263A", "PHE160274A", "PHE160287A", "PHE160294A", "PHE160316A", "PHE160317A", "PHE170372A", "PHE170386A", "PHE170397A", "PHE170405A", "TPA_BCC081", "TPA_BCC088", "TPA_BCC089", "TPA_BCC101", "TPA_BCC122", "TPA_BCC126", "TPA_BCC136", "TPA_BCC169", "TPA_HUN180004", "TPA_HUN190020", "TPA_UKBIR044", "TPA_UKBRG007", "TPA_UKBRG008")
# Get MRCA date for lower clade
sublineage.14.lowerclade.list.tmrca <- sublineage.14.tree.heatmap.data[sublineage.14.tree.heatmap.data$node==getMRCA(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 14)@phylo, sublineage.14.lowerclade.list),"x"]
paste0("TMRCA for sublineage 14 lower clade: ",sublineage.14.lowerclade.list.tmrca)[1] "TMRCA for sublineage 14 lower clade: 2006.53850498154"# Get MRCA date for upper clade
sublineage.14.upperclade.list.tmrca <- sublineage.14.tree.heatmap.data[sublineage.14.tree.heatmap.data$node==getMRCA(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 14)@phylo, sublineage.14.upperclade.list),"x"]
paste0("TMRCA for sublineage 14 upper clade: ",sublineage.14.upperclade.list.tmrca)[1] "TMRCA for sublineage 14 upper clade: 1999.15025243934"
Extract key information for sublineage 6 (two samples)
sublineage.6.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 6), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked)Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.sublineage.6.tree.heatmap.data <- sublineage.6.tree.heatmap$data
# Get MRCA date for upper clade
sublineage.6.beasttree.tmrca <- as.numeric(sublineage.6.tree.heatmap.data[sublineage.6.tree.heatmap.data$node==getMRCA(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 6)@phylo, c("PHE130048A", "PHE160283A")),"branch"])
paste0("TMRCA for sublineage 6 upper clade: ",sublineage.6.beasttree.tmrca)[1] "TMRCA for sublineage 6 upper clade: 1982.61865062176"
### Extract sample & population statistics from datasets for use in manuscript text
Dataset and Geographical distributions
# dataset counts
paste0("Total UK samples in cleaned/deduplicated dataset: ",nrow(PHE.metadata.linked))[1] "Total UK samples in cleaned/deduplicated dataset: 237"paste0("Of which: ",nrow(PHE.metadata.linked[PHE.metadata.linked$is.PHE=="PHE",])," from PHE Ref lab at Colindale")[1] "Of which: 195 from PHE Ref lab at Colindale"paste0("Of which: ",nrow(PHE.metadata.linked[PHE.metadata.linked$is.PHE=="Other",])," from other labs")[1] "Of which: 42 from other labs"# proportion with geographical data
paste0("From UK samples, ", nrow(PHE.metadata.linked[(PHE.metadata.linked$phe_centre %notin% c("Not Known","UK (not England)")),])," were grouped into one of the 9 PH regions")[1] "From UK samples, 217 were grouped into one of the 9 PH regions"paste0("From UK samples, ", nrow(PHE.metadata.linked[PHE.metadata.linked$phe_centre=="UK (not England)",]), " were referred from outside England")[1] "From UK samples, 2 were referred from outside England"paste0("From UK samples, ", nrow(PHE.metadata.linked[PHE.metadata.linked$phe_centre=="Not Known",]), " had unknown region")[1] "From UK samples, 18 had unknown region"# counts & fractions by PHE region
PHE.geo.countNA
Gender Orientation stats
PHE.orientation.countsPHE.geo.orientation.countsPHE.geo.HIV.countsPHE.sublineage.orientation.countsPHE.sublineage.Age
Sublineage Distributions
PHE.Lineage.countPHE.sublin.countPHE.geo.sublineage
Macrolide resistance stats
UK.macrolide.res <- PHE.metadata.linked %>%
dplyr::group_by(A2058G, A2059G) %>%
dplyr::summarise(Count.allele=n()) %>%
dplyr::ungroup() %>%
dplyr::mutate(total.count=sum(Count.allele), perc.allele=round((Count.allele/total.count)*100,1))`summarise()` has grouped output by 'A2058G'. You can override using the `.groups` argument.UK.macrolide.res
UK.macrolide.res.sublin <- PHE.metadata.linked %>%
dplyr::group_by(TPA.pinecone.sublineage, A2058G, A2059G) %>%
dplyr::summarise(Count.allele=n()) %>%
dplyr::ungroup() %>%
dplyr::group_by(TPA.pinecone.sublineage) %>%
dplyr::mutate(total.count=sum(Count.allele), perc.allele=round((Count.allele/total.count)*100,1))`summarise()` has grouped output by 'TPA.pinecone.sublineage', 'A2058G'. You can override using the `.groups` argument.UK.macrolide.res.sublin
# Calculate long form df, with different 23S alleles (A2058G, A2059G, WT, Uncertain) v.s. sublineage
UK.macrolide.res.sublin.long <- PHE.metadata.linked %>%
mutate(Resistance.allele=ifelse(A2058G=="Yes", "A2058G", ifelse(A2059G=="Yes", "A2059G", ifelse((A2058G=="No" & A2059G=="No"),"Wild Type", "Uncertain")))) %>%
dplyr::group_by(TPA.pinecone.sublineage, Resistance.allele) %>%
dplyr::summarise(Count.per.sublin.Macrolides=n()) %>%
dplyr::mutate(total.sublin=sum(Count.per.sublin.Macrolides),
fraction=Count.per.sublin.Macrolides/total.sublin) %>%
#dplyr::ungroup() %>%
dplyr::arrange((Resistance.allele), .by_group=T) %>%
dplyr::mutate(cum_fract = cumsum(fraction)) %>%
dplyr::mutate(cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(Resistance.allele = factor(Resistance.allele, levels=rev(c("A2058G", "A2059G", "Uncertain", "Wild Type"))))`summarise()` has grouped output by 'TPA.pinecone.sublineage'. You can override using the `.groups` argument.# Make plot of macrolide resistance by sublineages
p.sublin.Macrolides.hbarplot <- ggplot(UK.macrolide.res.sublin.long, aes(Count.per.sublin.Macrolides, y=TPA.pinecone.sublineage, fill=Resistance.allele)) +
geom_barh(stat="identity", position="fill", width=0.65) +
theme_light() +
scale_fill_manual(name="Macrolide\nResistance\nAllele",values=c("indianred2", "steelblue1","grey55", "grey90"), breaks=c("A2058G", "A2059G", "Uncertain", "Wild Type")) +
labs(y="TPA Sublineage", x="Proportion with Macrolide Resistance Allele") +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
guides(fill=guide_legend(ncol=2)) +
geom_text(data=UK.macrolide.res.sublin.long, aes(cum_fract.mid, y=TPA.pinecone.sublineage,label=Count.per.sublin.Macrolides), size=theme.text.size.within, inherit.aes = F) +
NULL
p.sublin.Macrolides.hbarplot
# Combine plot with sublineage count bars
p.sublin.Macrolides.hbarplot.combi <- plot_grid(p.sublineage.hbarplot + guides(fill=guide_legend(ncol=3)), p.sublin.Macrolides.hbarplot + y.theme.strip, nrow=1, align=T, labels=c("A", "B"), label_size=panel.lab.size)
p.sublin.Macrolides.hbarplot.combi
#ggsave(paste0(Figure_output_directory,"SupFig9_TPA-PHE_Sublin-Macrolide-Res.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=160, height=120, device='pdf', dpi=1200)
Pairwise SNP analysis
OK, want to investigate the different patterns observable for the North East of England (pale blue) in Sublineage 1
Multiple ways we can do this - including SNP distances (also multiple ways to do that)
###
#Use phylogenetic distance from the SNP scaled tree
TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist <- ape::cophenetic.phylo(TPA.pyjar.tree.subset.uk)
TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt <- data.frame(Taxa1=row.names(TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist), TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist, stringsAsFactors = F) %>% tidyr::gather(Taxa2, Distance.Phylo, -Taxa1)
# Taxa Comparisons label
TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt$Taxa_combination <- sapply(1:nrow(TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt), function (x) paste0(sort(c(as.character(TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt$Taxa1[x]),as.character(TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt$Taxa2[x]))),collapse="___"))
# Merge together
#TPA.WGS.alignment.data.dist.melt <- dplyr::left_join(TPA.WGS.alignment.data.dist.melt, TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt[,c("Taxa_combination","Distance.Phylo")], by="Taxa_combination")
TPA.WGS.alignment.data.dist.melt <- TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt
TPA.WGS.alignment.data.dist.melt <- unique(TPA.WGS.alignment.data.dist.melt)
Ok, now bring in some metadata and comparisons
# Bring in and merge metadata
PHE.meta.pairwise.t1 <- PHE.metadata.linked[,c("Sample_Name","year","phe_centre","london","gender_orientation","hivpos","age_group","ukborn","TPA.pinecone.sublineage", "TPA_Lineage","Geo_Country","is.UK","is.PHE", "Sample_Year","date.decimal")]
colnames(PHE.meta.pairwise.t1) <- paste0(colnames(PHE.meta.pairwise.t1),".t1")
colnames(PHE.meta.pairwise.t1)[1] <- "Taxa1"
PHE.meta.pairwise.t2 <- PHE.metadata.linked[,c("Sample_Name","year","phe_centre","london","gender_orientation","hivpos","age_group","ukborn","TPA.pinecone.sublineage", "TPA_Lineage","Geo_Country","is.UK","is.PHE", "Sample_Year","date.decimal")]
colnames(PHE.meta.pairwise.t2) <- paste0(colnames(PHE.meta.pairwise.t2),".t2")
colnames(PHE.meta.pairwise.t2)[1] <- "Taxa2"
PHE.alignment.data.dist.melt.meta <- plyr::join(TPA.WGS.alignment.data.dist.melt,PHE.meta.pairwise.t1, by="Taxa1", type="left")
PHE.alignment.data.dist.melt.meta <- plyr::join(PHE.alignment.data.dist.melt.meta,PHE.meta.pairwise.t2, by="Taxa2", type="left")
# Exclude missing data (e.g. missing sublineage) - this will also remove non-UK samples, since full metadata is missing here
PHE.alignment.data.dist.melt.meta <- PHE.alignment.data.dist.melt.meta[!is.na(PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t1),]
PHE.alignment.data.dist.melt.meta <- PHE.alignment.data.dist.melt.meta[!is.na(PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t2),]
Define comparisons
# Same sample
PHE.alignment.data.dist.melt.meta$same.sample <- ifelse(PHE.alignment.data.dist.melt.meta$Taxa1==PHE.alignment.data.dist.melt.meta$Taxa2,"same", "different")
# Years between samples
PHE.alignment.data.dist.melt.meta$year.distance <- abs(as.numeric(PHE.alignment.data.dist.melt.meta$year.t1) - as.numeric(PHE.alignment.data.dist.melt.meta$year.t2))
PHE.alignment.data.dist.melt.meta$Sample_Year.distance <- abs(as.numeric(PHE.alignment.data.dist.melt.meta$Sample_Year.t1) - as.numeric(PHE.alignment.data.dist.melt.meta$Sample_Year.t2))
# Years between decimal date (more precise temporal distance)
PHE.alignment.data.dist.melt.meta$decimal.date.distance <- abs(as.numeric(PHE.alignment.data.dist.melt.meta$date.decimal.t1) - as.numeric(PHE.alignment.data.dist.melt.meta$date.decimal.t2))
# Epidemiological time between - catagorical
PHE.alignment.data.dist.melt.meta$epi.time.distance.cat <- ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<1/12,"month", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=3/12, "quarter", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=6/12, "half year", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=1, "1 year",ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=2, "2 years", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=3, "3 years", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=4, "4 years", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=5, "5 years", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=6, "6 years",">6 years")))))))))
PHE.alignment.data.dist.melt.meta$epi.time.distance.cat <- factor(PHE.alignment.data.dist.melt.meta$epi.time.distance.cat, levels=c("month", "quarter","half year","1 year", "2 years", "3 years", "4 years", "5 years", "6 years", ">6 years"))
PHE.alignment.data.dist.melt.meta$epi.time.distance.cat.years <- ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=1, "0", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=2, "1", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=3, "2", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=4, "3", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=5, "4", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=6, "5",">5"))))))
# Same country
PHE.alignment.data.dist.melt.meta$same.country <- ifelse(PHE.alignment.data.dist.melt.meta$Geo_Country.t1 == PHE.alignment.data.dist.melt.meta$Geo_Country.t2, "same", "different")
# Is UK
PHE.alignment.data.dist.melt.meta$both.uk <- ifelse(PHE.alignment.data.dist.melt.meta$is.UK.t1 == PHE.alignment.data.dist.melt.meta$is.UK.t2, "same", "different")
# Is PHE
PHE.alignment.data.dist.melt.meta$both.PHE <- ifelse(PHE.alignment.data.dist.melt.meta$is.PHE.t1 == PHE.alignment.data.dist.melt.meta$is.PHE.t2, "same", "different")
# Same TPA Lineage (cleaned up classifications)
PHE.alignment.data.dist.melt.meta$same.TPA.Lineage <- ifelse(PHE.alignment.data.dist.melt.meta$TPA_Lineage.t1==PHE.alignment.data.dist.melt.meta$TPA_Lineage.t2, "same", "different")
PHE.alignment.data.dist.melt.meta$same.TPA.Lineage <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function(x) ifelse((PHE.alignment.data.dist.melt.meta$TPA_Lineage.t1[x]=="0" | PHE.alignment.data.dist.melt.meta$TPA_Lineage.t2[x]=="0"),NA,PHE.alignment.data.dist.melt.meta$same.TPA.Lineage[x]))
# Same TPA sublineage
PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster <- ifelse(PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t1==PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t2,"same", "different")
PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function(x) ifelse(((PHE.alignment.data.dist.melt.meta$same.sample[x]=="different" & PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t1[x]=="Singleton") |(PHE.alignment.data.dist.melt.meta$same.sample[x]=="different" & PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t2[x]=="Singleton")),"different",PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster[x]))
# Define Genetic relationships hierarchically
PHE.alignment.data.dist.melt.meta$genomic.cluster.hierarchy <- ifelse(PHE.alignment.data.dist.melt.meta$Distance==0,"Zero_SNPs", ifelse(PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster=="same","Same Sublineage", ifelse(PHE.alignment.data.dist.melt.meta$same.TPA.Lineage=="same", "Same Lineage","Different Lineage")))
PHE.alignment.data.dist.melt.meta$genomic.cluster.hierarchy.ph <- ifelse(PHE.alignment.data.dist.melt.meta$Distance.Phylo==0,"Zero_SNPs", ifelse(PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster=="same","Same Sublineage", ifelse(PHE.alignment.data.dist.melt.meta$same.TPA.Lineage=="same", "Same Lineage","Different Lineage")))
# Same PHE region
PHE.alignment.data.dist.melt.meta$same.PHE.region <- ifelse(PHE.alignment.data.dist.melt.meta$phe_centre.t1==PHE.alignment.data.dist.melt.meta$phe_centre.t2, "same", "different")
PHE.alignment.data.dist.melt.meta$PHE.centre.combination <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function (x) paste0(sort(c(as.character(PHE.alignment.data.dist.melt.meta$phe_centre.t1[x]),as.character(PHE.alignment.data.dist.melt.meta$phe_centre.t2[x]))),collapse="___"))
# does the combination included London?
PHE.alignment.data.dist.melt.meta$involves.London <- ifelse(PHE.alignment.data.dist.melt.meta$phe_centre.t1=="London" | PHE.alignment.data.dist.melt.meta$phe_centre.t2=="London", "London", "not-London")
# Orientation pair
PHE.alignment.data.dist.melt.meta$Orientation_combination <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function (x) paste0(sort(c(as.character(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]),as.character(PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]))),collapse="___"))
#PHE.alignment.data.dist.melt.meta$Orientation.Class <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function (x) ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="MSM" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="MSM", "MSM",
# ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="MSM" | PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="MSM", "Mixed",
# ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="MSW" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="WSM","Heterosexual",
# ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="WSM" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="MSW","Heterosexual","Unknown")))))
PHE.alignment.data.dist.melt.meta$Orientation.Class <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function (x) ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="GBMSM" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="GBMSM", "GBMSM",
ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x] %in% c("MSW","WSM") & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x] %in% c("MSW","WSM"),"Heterosexual",
ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="GBMSM" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x] %in% c("MSW","WSM"), "Mixed",
ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x] %in% c("MSW","WSM") & PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="GBMSM", "Mixed", "Unknown")))))
# Country Comparisons label
PHE.alignment.data.dist.melt.meta$Country_combinations <- paste0(PHE.alignment.data.dist.melt.meta$Geo_Country.t1,"___",PHE.alignment.data.dist.melt.meta$Geo_Country.t2)
# Subset to PHE data only (effectively already done, but let's be explicit)
PHE.TPA.alignment.data.dist.melt.meta <- PHE.alignment.data.dist.melt.meta[(PHE.alignment.data.dist.melt.meta$both.uk=="same" & PHE.alignment.data.dist.melt.meta$both.PHE=="same"),]
PHE.TPA.alignment.data.dist.melt.meta <- PHE.TPA.alignment.data.dist.melt.meta[PHE.TPA.alignment.data.dist.melt.meta$PHE.only=="PHE",]
PHE.TPA.alignment.data.dist.melt.meta <- PHE.alignment.data.dist.melt.meta[(PHE.alignment.data.dist.melt.meta$both.uk=="same"),]
# Make single sided
PHE.TPA.alignment.data.dist.melt.meta <- PHE.TPA.alignment.data.dist.melt.meta[!duplicated(PHE.TPA.alignment.data.dist.melt.meta$Taxa_combination),]
### Perform a more detailed analysis of samples from the North East of England
Do a more detailed exploration of the North East of England
PHE.metadata.linked2.region_NorthEast <- PHE.metadata.linked[PHE.metadata.linked$phe_centre=="North East",]
# Constrain by samples being from the North East
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta[(PHE.alignment.data.dist.melt.meta$phe_centre.t1=="North East" & PHE.alignment.data.dist.melt.meta$same.sample=="different"),]
# Constrain by the same PHE region
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[PHE.alignment.data.dist.melt.meta.NorthEast.clusters$same.PHE.region=="same",]
#Just plot these distros
p.NorthEast.Pairwise.SNPs.unconstrained <- ggplot(PHE.alignment.data.dist.melt.meta.NorthEast.clusters, aes(Distance.Phylo)) +
geom_histogram(binwidth = 1) +
theme_bw() +
theme.text.size +
labs(x="Pairwise SNP Distance", y="Comparison Count")
p.NorthEast.Pairwise.SNPs.unconstrained
Make a single linkage network from the North East samples
# Constrain by SNP distance (looser than previously - we just want to find basic groupings within sublineage 1 for NE samples)
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[PHE.alignment.data.dist.melt.meta.NorthEast.clusters$Distance.Phylo<=2,]
# And make sure that we actually have genetic distance data for all samples within the network
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[!is.na(PHE.alignment.data.dist.melt.meta.NorthEast.clusters$Distance.Phylo),]
# cleanup some data noise
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[!is.na(PHE.alignment.data.dist.melt.meta.NorthEast.clusters$year.t1),]
# prepare intput data (with edge info)
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1 <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[,c("Taxa1","Taxa2","Distance.Phylo","decimal.date.distance","year.distance","Orientation.Class","epi.time.distance.cat")]
############
# some issues with update to R4 - double sided matrix
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$edgename <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1), function(x) paste0(sort(as.character(unlist(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1[x,c("Taxa1","Taxa2")]))),collapse="___"))
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1 <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1[!duplicated(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$edgename),]
# Also having an issue with taxa as factors here
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Taxa1 <- as.character(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Taxa1)
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Taxa2 <- as.character(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Taxa2)
############
#inverse weight
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Distance.inv <- 1/PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Distance.Phylo
# Make actual network
set.seed(1235)
PHE.NorthEast.network <- network(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1, matrix.type = "edgelist", ignore.eval = FALSE, directed = F)
PHE.NorthEast.network.gg <- ggnetwork(PHE.NorthEast.network, layout = "kamadakawai", weights = "Distance.inv")
PHE.NorthEast.network.gg$Taxa1 <- PHE.NorthEast.network.gg$vertex.names
# extract temporal clusters from network
PHE.NorthEast.network.ig <- asIgraph(PHE.NorthEast.network)
PHE.NorthEast.network.components <- data.frame(Taxa1=network.vertex.names(PHE.NorthEast.network), vertex.no=as.vector(V(PHE.NorthEast.network.ig)), cluster=igraph::components(PHE.NorthEast.network.ig)$membership)
# For ease of story telling in the paper, flip clusters 2 and 3 around (so we can talk about 2 first)
PHE.NorthEast.network.components <- PHE.NorthEast.network.components %>%
dplyr::mutate(cluster.old=cluster, cluster=ifelse(cluster.old==2, 3, ifelse(cluster.old==3,2,cluster.old)))
PHE.NorthEast.network.components$Cluster <- paste0("Cluster",PHE.NorthEast.network.components$cluster)
# merge metadata back in
PHE.NorthEast.network.gg <- plyr::join(PHE.NorthEast.network.gg, data.frame(Taxa1=PHE.metadata.linked$Sample_Name, PHE.metadata.linked[,c("phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], stringsAsFactors = F),by="Taxa1", type="left")
PHE.NorthEast.network.gg <- plyr::join(PHE.NorthEast.network.gg, data.frame(Taxa1=PHE.NorthEast.network.components$Taxa1, Cluster=PHE.NorthEast.network.components$Cluster), by="Taxa1", type="left")
Plot network
# Plot network
p.PHE.NorthEast.network.2SNP <- ggplot(PHE.NorthEast.network.gg, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(alpha=0.90, curvature = 0.2, aes(color=factor(Distance.Phylo), linetype=factor(Distance.Phylo))) +
scale_color_manual(values=c("grey5","grey55","grey85"), name="SNP\nDistance") +
scale_linetype(name="SNP\nDistance") +
theme_blank() +
ggnewscale::new_scale_color() + ggnewscale::new_scale("size") +
geom_nodelabel(aes(color=gender_orientation, label=paste(Taxa1,year,sep="\n"),fontface = "bold"), alpha=0.8, size=theme.text.size.within-0.4, label.size=0.15, label.padding = unit(0.05, "lines")) +
geom_nodes(size=1.0, aes(color=gender_orientation)) +
scale_color_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
NULL
p.PHE.NorthEast.network.2SNP
Ok, so three networks. Clear differentiation of a heterosexual network (with 0-snp distances) and two predominantly MSM networks
Let’s look at the phylogenetic context of those North East clusters we’ve defined. Pull out subtrees (from sublineage 1 subtree)
# Cluster 1
Beast.tree.NE.cluster1 <- getMRCA(full.beast2.tree@phylo, PHE.NorthEast.network.components[PHE.NorthEast.network.components$Cluster=="Cluster1","Taxa1"])
Beast.tree.NE.cluster1.subtree <- tree_subset(full.beast2.tree, node=Beast.tree.NE.cluster1, levels_back=0)
p.Beast.tree.NE.cluster1.subtree <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Beast.tree.NE.cluster1.subtree, TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, initial.track.offset = 10)Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# Can't fit in tip labs, but since this is a polyphyletic subtree, it would be helpful to add a track to highlight the NE strains
PHE.metadata.linked$is.NorthEast <- ifelse(PHE.metadata.linked$phe_centre=="North East","North East", "Other England")
p.Beast.tree.NE.cluster1.subtree.cluster.highlight <- gheatmap(p.Beast.tree.NE.cluster1.subtree, data.frame(row.names=PHE.metadata.linked$Sample_Name, `North East`=PHE.metadata.linked$is.NorthEast), color=NULL,width=(1/max(p.Beast.tree.NE.cluster1.subtree$data$height)*3), offset=10+(4*5),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
scale_fill_manual(name="North East\nEngland", values=c("#A6CEE3","grey95"), breaks=c("North East","Other England"), na.value = "white", guide = guide_legend(order = 5)) +
ggnewscale::new_scale_fill()Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# Just confirm the ClusterIDs for this subtree (make sure it doesn't enclose other clusters)
p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID <- gheatmap(p.Beast.tree.NE.cluster1.subtree.cluster.highlight, data.frame(row.names=PHE.NorthEast.network.components$Taxa1, ClusterID=PHE.NorthEast.network.components$Cluster), color=NULL,width=(1/max(p.Beast.tree.NE.cluster1.subtree$data$height)*3), offset=10+(4*6),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
scale_fill_manual(name="North East\nCluster", values=c("#7fc97f","#beaed4","#fdc086"), breaks=c("Cluster1","Cluster2","Cluster3"), na.value = "white", guide = guide_legend(order = 6)) +
ggnewscale::new_scale_fill()Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# add a bit more room to the x axis
p.Beast.tree.NE.cluster1.subtree.cluster.highlight.x.axis.limits <- ggplot_build(p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID)$layout$panel_scales_x[[1]]$range$range
p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID <- p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID +
coord_cartesian(x=c(p.Beast.tree.NE.cluster1.subtree.cluster.highlight.x.axis.limits[1],p.Beast.tree.NE.cluster1.subtree.cluster.highlight.x.axis.limits[2]+4), y=c(-0.5-(length(unique(p.Beast.tree.NE.cluster1.subtree.cluster.highlight$data$label))/15),length(unique(p.Beast.tree.NE.cluster1.subtree.cluster.highlight$data$label))+2)) +
theme(legend.margin = margin(-0.5,0,0,0, unit="mm"))Coordinate system already present. Adding new coordinate system, which will replace the existing one.#p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID
#######################
# Cluster 2
Beast.tree.NE.cluster2 <- getMRCA(full.beast2.tree@phylo, PHE.NorthEast.network.components[PHE.NorthEast.network.components$Cluster=="Cluster2","Taxa1"])
Beast.tree.NE.cluster2.subtree <- tree_subset(full.beast2.tree, node=Beast.tree.NE.cluster2, levels_back=1)
p.Beast.tree.NE.cluster2.subtree <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Beast.tree.NE.cluster2.subtree, TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, initial.track.offset = 20) + geom_tiplab(size=theme.text.size.within, align=T, offset=5, linesize=0.4)Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# Just add ClusterIDs for this subtree to highlight
p.Beast.tree.NE.cluster2.subtree <- gheatmap(p.Beast.tree.NE.cluster2.subtree, data.frame(row.names=PHE.NorthEast.network.components$Taxa1, ClusterID=PHE.NorthEast.network.components$Cluster), color=NULL,width=(1/max(p.Beast.tree.NE.cluster2.subtree$data$height)*3), offset=20+(4*5),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
scale_fill_manual(name="North East\nCluster", values=c("#7fc97f","#beaed4","#fdc086"), breaks=c("Cluster1","Cluster2","Cluster3"), na.value = "white", guide = guide_legend(order = 5, ncol=2)) +
ggnewscale::new_scale_fill()Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# add a bit more room to the x axis
p.Beast.tree.NE.cluster2.subtree.x.axis.limits <- ggplot_build(p.Beast.tree.NE.cluster2.subtree)$layout$panel_scales_x[[1]]$range$range
p.Beast.tree.NE.cluster2.subtree <- p.Beast.tree.NE.cluster2.subtree +
coord_cartesian(x=c(p.Beast.tree.NE.cluster2.subtree.x.axis.limits[1],p.Beast.tree.NE.cluster2.subtree.x.axis.limits[2]+12), y=c(-0.5-(length(unique(p.Beast.tree.NE.cluster2.subtree$data$label))/20)-1,length(unique(p.Beast.tree.NE.cluster2.subtree$data$label))+0.5)) +
theme(legend.margin = margin(-0.5,0,0,0, unit="mm"))Coordinate system already present. Adding new coordinate system, which will replace the existing one.#p.Beast.tree.NE.cluster2.subtree
############################
# Cluster 3
Beast.tree.NE.cluster3 <- getMRCA(full.beast2.tree@phylo, PHE.NorthEast.network.components[PHE.NorthEast.network.components$Cluster=="Cluster3","Taxa1"])
Beast.tree.NE.cluster3.subtree <- tree_subset(full.beast2.tree, node=Beast.tree.NE.cluster3, levels_back=1)
p.Beast.tree.NE.cluster3.subtree <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Beast.tree.NE.cluster3.subtree, TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, initial.track.offset = 26) + geom_tiplab(size=theme.text.size.within, align=T, offset=3, linesize=0.4)Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.
Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# Just add ClusterIDs for this subtree to highlight
p.Beast.tree.NE.cluster3.subtree <- gheatmap(p.Beast.tree.NE.cluster3.subtree, data.frame(row.names=PHE.NorthEast.network.components$Taxa1, ClusterID=PHE.NorthEast.network.components$Cluster), color=NULL,width=(1/max(p.Beast.tree.NE.cluster3.subtree$data$height)*3), offset=26+(4*5),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
scale_fill_manual(name="North East\nCluster", values=c("#7fc97f","#beaed4","#fdc086"), breaks=c("Cluster1","Cluster2","Cluster3"), na.value = "white", guide = guide_legend(order = 5, ncol=2)) +
ggnewscale::new_scale_fill()Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# add a bit more room to the x axis
p.Beast.tree.NE.cluster3.subtree.x.axis.limits <- ggplot_build(p.Beast.tree.NE.cluster3.subtree)$layout$panel_scales_x[[1]]$range$range
p.Beast.tree.NE.cluster3.subtree <- p.Beast.tree.NE.cluster3.subtree +
coord_cartesian(x=c(p.Beast.tree.NE.cluster3.subtree.x.axis.limits[1],p.Beast.tree.NE.cluster3.subtree.x.axis.limits[2]+12), y=c(-0.5-(length(unique(p.Beast.tree.NE.cluster3.subtree$data$label))/20)-1,length(unique(p.Beast.tree.NE.cluster3.subtree$data$label))+0.5)) +
theme(legend.margin = margin(-0.5,0,0,0, unit="mm"))Coordinate system already present. Adding new coordinate system, which will replace the existing one.#p.Beast.tree.NE.cluster3.subtree
#p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID
#p.Beast.tree.NE.cluster2.subtree
#p.Beast.tree.NE.cluster3.subtree
Since Cluster 1 is really quite polyphyletic, it maybe more useful to show the clusters in context for that one
# Add North East identifier column
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight <- gheatmap(sublineage.1.tree.heatmap, data.frame(row.names=PHE.metadata.linked$Sample_Name, `North East`=PHE.metadata.linked$is.NorthEast), color=NULL,width=(1/max(sublineage.1.tree.heatmap$data$height)*3)*1.2, offset=0+(4*5)*1.2,colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
scale_fill_manual(name="North East\nEngland", values=c("#A6CEE3","grey95"), breaks=c("North East","Other England"), na.value = "white", guide = guide_legend(order = 5)) +
ggnewscale::new_scale_fill()Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# Just confirm the ClusterIDs for this subtree (make sure it doesn't enclose other clusters)
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight <- gheatmap(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight, data.frame(row.names=PHE.NorthEast.network.components$Taxa1, ClusterID=PHE.NorthEast.network.components$Cluster), color=NULL,width=(1/max(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight$data$height)*3)*1.2, offset=0+(4*6)*1.2,colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
scale_fill_manual(name="North East\nCluster", values=c("#7fc97f","#beaed4","#fdc086"), breaks=c("Cluster1","Cluster2","Cluster3"), na.value = "white", guide = guide_legend(order = 6, ncol=2)) +
ggnewscale::new_scale_fill()Scale for 'fill' is already present. Adding another scale for 'fill', which will replace the existing scale.# add a bit more room to the x axis
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight.x.axis.limits <- ggplot_build(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight)$layout$panel_scales_x[[1]]$range$range
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight <- p.Beast.tree.sublineage1.NE.subtree.cluster.highlight +
coord_cartesian(x=c(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight.x.axis.limits[1],p.Beast.tree.sublineage1.NE.subtree.cluster.highlight.x.axis.limits[2]+4), y=c(-0.5-(length(unique(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight$data$label))/15),length(unique(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight$data$label))+2))Coordinate system already present. Adding new coordinate system, which will replace the existing one.# reduce spacing between legend scales
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight <- p.Beast.tree.sublineage1.NE.subtree.cluster.highlight + theme(legend.margin = margin(-0.95,0,0,0, unit="mm"))
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight
Plot together
p.Beast.tree.NE.subtrees.combi1 <- plot_grid(p.Beast.tree.NE.cluster2.subtree, p.Beast.tree.NE.cluster3.subtree, ncol=1, labels=c("C - Cluster 2", "D - Cluster 3"), vjust=1.0, label_size=panel.lab.size, scale=0.95)
p.Beast.tree.NE.subtrees.combi2 <- plot_grid(p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID, p.Beast.tree.NE.subtrees.combi1, ncol=2, rel_widths=c(3,2), labels=c("B - Cluster 1", ""), label_size=panel.lab.size)
p.Beast.tree.NE.subtrees.combi2
p.Beast.tree.NE.subtrees.combi3 <- plot_grid(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight, p.Beast.tree.NE.subtrees.combi1, ncol=2, rel_widths=c(8,7), labels=c("B - Sublineage 1 (All)", ""), label_size=panel.lab.size, scale=0.95, vjust=1.0)
p.Beast.tree.NE.subtrees.combi3
Look more closely at population demographics of these clusters
# Metadata on NE cluster 2
PHE.metadata.linked %>%
dplyr::filter(Sample_Name %in% Beast.tree.NE.cluster2.subtree@phylo$tip.label) %>%
dplyr::group_by(Geo_Country, is.NorthEast, gender_orientation) %>%
dplyr::summarise(Count=n())`summarise()` has grouped output by 'Geo_Country', 'is.NorthEast'. You can override using the `.groups` argument.# Metadata on NE cluster 3
PHE.metadata.linked %>%
dplyr::filter(Sample_Name %in% Beast.tree.NE.cluster3.subtree@phylo$tip.label) %>%
dplyr::group_by(Geo_Country, is.NorthEast, gender_orientation) %>%
dplyr::summarise(Count=n())`summarise()` has grouped output by 'Geo_Country', 'is.NorthEast'. You can override using the `.groups` argument.# Country info on NE cluster 3
TPA.meta2.1 %>%
dplyr::filter(Sample_Name %in% Beast.tree.NE.cluster3.subtree@phylo$tip.label) %>%
dplyr::group_by(Geo_Country) %>%
dplyr::summarise(Count=n())
# Separate metadata records show Hungarian sample "TPA_HUN180001" came from a male bisexual (MSWM).
Examine SNP scaled tree for distances
# Extract information about SNP distances
TPA.NEcluster3.pyjartree.mrca <- getMRCA(TPA.pyjar.tree, as.character(unlist(TPA.meta2.1[TPA.meta2.1$Sample_Name %in% Beast.tree.NE.cluster3.subtree@phylo$tip.label,"Sample_Name"])))
TPA.NEcluster3.pyjartree.subtree <- tree_subset(TPA.pyjar.tree, node=TPA.NEcluster3.pyjartree.mrca, levels_back=1)
ggtree(TPA.NEcluster3.pyjartree.subtree) + geom_tiplab(size=theme.text.size.within)
ggtree(TPA.NEcluster3.pyjartree.subtree)$data
Do some analysis of nearest neighbour and distances to MRCAs
calculate.years.from.mrca <- function(current.ggtree.phylo, current.ggtree.data){
#current.ggtree <- Beast.tree.NE.cluster3.subtree
all.tips <- current.ggtree.phylo$tip.label
dist.2.mrca <- NULL
### put dates into df
current.ggtree.data$mrca.median <- 2019.5 - current.ggtree.data$height_median
current.ggtree.data$year <- as.numeric(round(2019.5 - current.ggtree.data$height_median,3))
current.ggtree.data$mrca.95high <- round(2019.5 - sapply(1:nrow(current.ggtree.data),function(x) as.numeric(unlist(current.ggtree.data[x,"height_0.95_HPD"]))[1]), 3)
current.ggtree.data$mrca.95low <- round(2019.5 - sapply(1:nrow(current.ggtree.data),function(x) as.numeric(unlist(current.ggtree.data[x,"height_0.95_HPD"]))[2]), 3)
# extract dates between sample and its MRCA using loop
for (current.node in all.tips) {
current.parent <- c(match(current.node,current.ggtree.phylo$tip.label), phangorn::Ancestors(current.ggtree.phylo, match(c(current.node), current.ggtree.phylo$tip.label), "parent"))
current.nodelist <- current.ggtree.data[current.ggtree.data$node %in% current.parent,]
current.dist.2.mrca <- c(current.node, as.numeric(current.nodelist[1,"year"]-current.nodelist[2,"year"]))
dist.2.mrca <- rbind(dist.2.mrca, current.dist.2.mrca)
}
dist.2.mrca <- data.frame(Sample_Name=as.character(dist.2.mrca[,1]), dist.to.mrca=as.numeric(dist.2.mrca[,2]), stringsAsFactors=F)
return(dist.2.mrca)
}
### All samples in global tree
dist.mrca.all.TPA <- calculate.years.from.mrca(full.beast2.tree@phylo, full.beast2.tree@data)
Merge dist2MRCA with metadata
PHE.metadata.linked.dist2mrca <- left_join(PHE.metadata.linked, dist.mrca.all.TPA, by="Sample_Name")
p.time2mrca.orientation <- ggplot(PHE.metadata.linked.dist2mrca, aes(gender_orientation, dist.to.mrca, color=gender_orientation)) +
geom_quasirandom(size=0.75, alpha=0.5) +
theme_light() + theme.text.size +
coord_flip() +
labs(x="Gender Orientation", y="Years to MRCA", color="Gender Orientation") +
theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
scale_color_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation)
p.time2mrca.phe_region <- ggplot(PHE.metadata.linked.dist2mrca, aes(phe_centre, dist.to.mrca, color=phe_centre)) +
geom_quasirandom(size=0.75, alpha=0.5) +
theme_light() + theme.text.size +
coord_flip(ylim=c(0,40)) +
labs(x="UKHSA Region", y="Years to MRCA", color="UKHSA Region") +
theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
scale_color_manual(name="UKHSA\nRegion", values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region)
p.time2mrca.phe_region.orientation <- ggplot(PHE.metadata.linked.dist2mrca, aes(phe_centre, dist.to.mrca, color=gender_orientation)) +
geom_quasirandom(size=0.75, alpha=0.5) +
theme_light() + theme.text.size +
coord_flip(ylim=c(0,20)) +
labs(x="UKHSA Region", y="Years to MRCA") +
theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
scale_color_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation)
p.time2mrca.phe_region.orientationWarning: Removed 15 rows containing missing values (position_quasirandom).
p.time2mrca.sublineage <- ggplot(PHE.metadata.linked.dist2mrca, aes(TPA.pinecone.sublineage, dist.to.mrca, color=TPA.pinecone.sublineage)) +
geom_quasirandom(size=0.75, alpha=0.5) +
theme_light() + theme.text.size +
coord_flip() +
labs(x="TPA Lineage", y="Years to MRCA", color="TPA Lineage") +
theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
scale_color_manual(values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage)
p.time2mrca.sublineageWarning: Removed 15 rows containing missing values (position_quasirandom).
p.time2mrca.Lineage <- ggplot(PHE.metadata.linked.dist2mrca, aes(TPA_Lineage, dist.to.mrca, color=TPA_Lineage)) +
geom_quasirandom(size=0.75, alpha=0.5) +
theme_light() + theme.text.size +
coord_flip() +
labs(x="TPA Lineage", y="Years to MRCA (Median of Posterior)", color="TPA Lineage") +
theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
scale_color_manual(values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage)
Maybe can make an MST of the North East samples for grapetree?
TPA.pyjar.tree.subset.NorthEast <- ape::keep.tip(TPA.pyjar.tree, as.character(unlist(PHE.metadata.linked[PHE.metadata.linked$phe_centre=="North East","Sample_Name"])))
#ggtree(TPA.pyjar.tree.subset.NorthEast)
#write.tree(TPA.pyjar.tree.subset.NorthEast, paste0(Data_input_directory,"TPA.UK-only-NorthEast.pyjar.2022-02-26.tre"))
# Write out a metadata sheet for the relevant information
PHE.metadata.linked.grapetree <- PHE.metadata.linked[,c("Sample_Name", "year","gender_orientation","phe_centre","hivpos","ukborn","TPA_Lineage","TPA.pinecone.sublineage")]
colnames(PHE.metadata.linked.grapetree)[1] <- "ID"
#write.table(PHE.metadata.linked.grapetree, paste0(Data_input_directory,"TPA.UK-only.grapetree.meta.2022-02-03.tsv"), sep = "\t", quote=F, row.names = F)Alternative approach using MST instead of networks for North East data
# Read in MST
#TPA.NorthEastEngland.Grapetree.file <- paste0(Data_input_directory,"TPA-UK-NorthEast-2022-02-26.GenderOrientation-MSTree.inkscaped.+node-counts+GBMSM.svg")
p.TPA.NorthEastEngland.Grapetree <- ggdraw() + draw_image(TPA.NorthEastEngland.Grapetree.file)
p.TPA.NorthEastEngland.Grapetree
p.TPA.NorthEastEngland.Grapetree.header <- plot_grid(p.TPA.NorthEastEngland.Grapetree, labels=c("A - Network Clusters (North East England)"), label_size=panel.lab.size, scale=0.95)
Plot with beast trees
#p.PHE.NorthEast_MST.with.beast.subtrees.combi <- plot_grid(p.TPA.NorthEastEngland.Grapetree, p.Beast.tree.NE.subtrees.combi3, ncol=1, rel_heights=c(3,6), labels=c("A - Network Clusters (North East England)", ""), label_size=panel.lab.size, scale = 0.95)
p.PHE.NorthEast_MST.with.beast.subtrees.combi <- plot_grid(p.TPA.NorthEastEngland.Grapetree.header, p.Beast.tree.NE.subtrees.combi3, ncol=1, rel_heights=c(3,7))
p.PHE.NorthEast_MST.with.beast.subtrees.combi
#ggsave(paste0(Figure_output_directory,"Fig3_Sublin1.NorthEast.MST+Beast.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=200, height=245, device='pdf', dpi=1200)
Do some analysis of major sublineages over time by region - could this influence observations about sublineages?
# Generate some stats by PHE Region
PHE.major.sublineage.PHEcentre.date <- PHE.metadata.linked %>%
dplyr::filter(TPA.pinecone.sublineage %in% c(1,14)) %>%
dplyr::group_by(TPA.pinecone.sublineage, phe_centre, year) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.sublin=sum(Count)) %>%
dplyr::arrange(desc(phe_centre), .by_group=T) %>%
dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))`summarise()` has grouped output by 'TPA.pinecone.sublineage', 'phe_centre'. You can override using the `.groups` argument.ggplot(PHE.major.sublineage.PHEcentre.date, aes(year, phe_centre, size=Count, color=TPA.pinecone.sublineage)) +
geom_point() +
facet_grid(.~TPA.pinecone.sublineage) +
theme_light() +
theme.text.size +
scale_color_manual(values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage)
p.PHE.major.sublineage.PHEcentre.date.bubbleplot <- ggplot(PHE.major.sublineage.PHEcentre.date, aes(year, TPA.pinecone.sublineage, color=TPA.pinecone.sublineage)) +
geom_point(alpha=0.65, aes(size=Count)) +
geom_line(alpha=0.25) +
facet_grid(factor(gsub("\\ ","\n",phe_centre), levels=gsub("\\ ","\n",PHE.region.cols.brew$UKHSA.region))~., switch='y') +
theme_light() +
theme(strip.placement = "outside") +
theme(strip.background = element_rect(color='white', fill='white',linetype="solid"), strip.text.y=element_text(color = "grey25",angle=0, size=5)) +
scale_size_area(max_size = 4.5,breaks=c(1,5,10,20,30,40)) +
theme.text.size +
scale_color_manual(values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage) +
labs(y="Region", x="Year", color="Sublineage")
p.PHE.major.sublineage.PHEcentre.date.bubbleplotgeom_path: Each group consists of only one observation. Do you need to adjust the group aesthetic?
Do some specific analysis for the 3 Northern regions
# Generate some stats by PHE Region
PHE.metadata.linked %>%
dplyr::filter(phe_centre %in% c("North East", "North West", "Yorkshire and Humber")) %>%
dplyr::summarise(count=n())
PHE.metadata.linked %>%
dplyr::filter(phe_centre %in% c("North East", "North West", "Yorkshire and Humber")) %>%
dplyr::group_by(year) %>%
dplyr::summarise(count=n())
p.PHE.major.sublineage.3NorthernRegions <- PHE.metadata.linked %>%
dplyr::filter(phe_centre %in% c("North East", "North West", "Yorkshire and Humber")) %>%
dplyr::group_by(TPA.pinecone.sublineage, year, phe_centre) %>%
dplyr::summarise(Count=n()) %>%
ggplot(aes(year, Count, fill=phe_centre)) +
geom_bar(stat='identity', width=0.65) +
scale_fill_manual(values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region) +
theme_bw() + theme.text.size +
scale_x_continuous(breaks=seq(2012,2018,1)) +
scale_y_continuous(breaks=pretty) +
labs(title="Samples in 3 Northern Regions", x="Collection Year", y="Sample Count", fill="Public Health\nRegion") +
theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
#geom_text(aes(x=year,y=Count-0.5, label=Count), color='grey95', size=theme.text.size.within) +
NULL`summarise()` has grouped output by 'TPA.pinecone.sublineage', 'year'. You can override using the `.groups` argument.p.PHE.major.sublineage.3NorthernRegions
Single linkage network of identical genomes from UK
# Constrain by SNP distance (identical in the asr snp tree)
PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta[PHE.alignment.data.dist.melt.meta$Distance.Phylo==0,]
# and a max of 2 years
#PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta.identicals[PHE.alignment.data.dist.melt.meta.identicals$decimal.date.distance<=2,]
# And make sure that we actually have genetic distance data for all samples within the network
PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta.identicals[!is.na(PHE.alignment.data.dist.melt.meta.identicals$Distance.Phylo),]
# remove self-samples
PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta.identicals[PHE.alignment.data.dist.melt.meta.identicals$same.sample=="different",]
# cleanup some data noise
PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta.identicals[!is.na(PHE.alignment.data.dist.melt.meta.identicals$year.t1),]
# prepare intput data (with edge info)
PHE.alignment.data.dist.melt.meta.identicals.input1 <- PHE.alignment.data.dist.melt.meta.identicals[,c("Taxa1","Taxa2","Distance.Phylo","decimal.date.distance","year.distance","Orientation.Class","epi.time.distance.cat.years","epi.time.distance.cat")]
############
# some issues with update to R4 - double sided matrix
PHE.alignment.data.dist.melt.meta.identicals.input1$edgename <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta.identicals.input1), function(x) paste0(sort(as.character(unlist(PHE.alignment.data.dist.melt.meta.identicals.input1[x,c("Taxa1","Taxa2")]))),collapse="___"))
PHE.alignment.data.dist.melt.meta.identicals.input1 <- PHE.alignment.data.dist.melt.meta.identicals.input1[!duplicated(PHE.alignment.data.dist.melt.meta.identicals.input1$edgename),]
# Also having an issue with taxa as factors here
PHE.alignment.data.dist.melt.meta.identicals.input1$Taxa1 <- as.character(PHE.alignment.data.dist.melt.meta.identicals.input1$Taxa1)
PHE.alignment.data.dist.melt.meta.identicals.input1$Taxa2 <- as.character(PHE.alignment.data.dist.melt.meta.identicals.input1$Taxa2)
############
# Deduplicate
#inverse weight
PHE.alignment.data.dist.melt.meta.identicals.input1$decimal.date.distance.inv <- 1/1/(PHE.alignment.data.dist.melt.meta.identicals.input1$decimal.date.distance+0.04)
# Make actual network
set.seed(1236)
PHE.identicals.network <- network(PHE.alignment.data.dist.melt.meta.identicals.input1, matrix.type = "edgelist", ignore.eval = FALSE, directed = F, loops = F)
#PHE.identicals.network.gg <- ggnetwork(PHE.identicals.network, layout = "kamadakawai", weights = "decimal.date.distance.inv")
#PHE.identicals.network.gg <- ggnetwork(PHE.identicals.network, layout = "fruchtermanreingold", weights = "decimal.date.distance")
PHE.identicals.network.gg <- ggnetwork(PHE.identicals.network, layout = "fruchtermanreingold")
PHE.identicals.network.gg$Taxa1 <- PHE.identicals.network.gg$vertex.names
# extract temporal clusters from network
PHE.identicals.network.ig <- asIgraph(PHE.identicals.network)
PHE.identicals.network.components <- data.frame(Taxa1=network.vertex.names(PHE.identicals.network), vertex.no=as.vector(V(PHE.identicals.network.ig)), cluster=igraph::components(PHE.identicals.network.ig)$membership)
PHE.identicals.network.components$Cluster <- paste0("Cluster",PHE.identicals.network.components$cluster)
# merge metadata back in
PHE.identicals.network.gg <- plyr::join(PHE.identicals.network.gg, data.frame(Taxa1=PHE.metadata.linked$Sample_Name, PHE.metadata.linked[,c("phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], stringsAsFactors = F),by="Taxa1", type="left")
PHE.identicals.network.gg <- plyr::join(PHE.identicals.network.gg, data.frame(Taxa1=PHE.identicals.network.components$Taxa1, Cluster=PHE.identicals.network.components$Cluster), by="Taxa1", type="left")
#
# Add temporal colour scale
#unique(PHE.identicals.network.gg$epi.time.distance.cat)
epi.time.distance.cat.cols <- rev(colorRampPalette(brewer.pal(8, "Greys"))(length(unique(PHE.identicals.network.gg$epi.time.distance.cat))-1))
# Plot network
p.PHE.identicals.network.0SNP <- ggplot(PHE.identicals.network.gg, aes(x = x, y = y, xend = xend, yend = yend)) +
geom_edges(alpha=0.90, curvature = 0.2, aes(color=factor(epi.time.distance.cat), linetype=factor(epi.time.distance.cat))) +
#scale_color_manual(values=c("grey5","grey35","grey55", "grey65", "grey75"), name="SNP\nDistance") +
scale_color_manual(name="Temporal\nDistance", values = epi.time.distance.cat.cols) +
scale_linetype(name="Temporal\nDistance") +
theme_blank() +
ggnewscale::new_scale_color() + ggnewscale::new_scale("size") +
#geom_nodelabel(aes(color=gender_orientation, label=paste(Taxa1,year,sep="\n"),fontface = "bold"), alpha=0.8, size=theme.text.size.within-0.4, label.size=0.15, label.padding = unit(0.05, "lines")) +
geom_nodes(size=2.5, aes(color=gender_orientation), alpha=0.9) +
scale_color_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
NULL
p.PHE.identicals.network.0SNP
Plot this against a UK tree?
gheatmap(ggtree(TPA.pyjar.tree.subset.uk),
data.frame(row.names=PHE.identicals.network.components$Taxa1, Cluster=PHE.identicals.network.components$Cluster))
Some stats from this
p.PHE.identical.Orientation_class.bydatedist <- PHE.alignment.data.dist.melt.meta %>%
dplyr::filter(same.sample=="different", Distance.Phylo==0) %>%
#filter(decimal.date.distance<=1) %>%
dplyr::group_by(epi.time.distance.cat, Orientation.Class) %>%
dplyr::summarise(Count.class.date=n()) %>%
dplyr::mutate(sum.class=sum(Count.class.date), fract.class=Count.class.date/sum.class) %>%
ggplot(aes(x=epi.time.distance.cat, y=Count.class.date, fill=Orientation.Class)) +
geom_bar(stat='identity', position='stack') +
theme_bw() +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
labs(x="Time between samples", y="Interaction Count", fill="Orientation Type")`summarise()` has grouped output by 'epi.time.distance.cat'. You can override using the `.groups` argument.p.PHE.identical.Orientation_class.bydatedist
p.PHE.identical.Orientation_class.byZerodist.cluster <- PHE.identicals.network.gg %>%
dplyr::filter(!is.na(Orientation.Class)) %>%
dplyr::group_by(Cluster, Orientation.Class) %>%
dplyr::summarise(Count.class.cluster=n()) %>%
dplyr::mutate(sum.class=sum(Count.class.cluster), fract.class=Count.class.cluster/sum.class) %>%
dplyr::arrange(desc(sum.class)) %>%
dplyr::ungroup() %>%
dplyr::mutate(Cluster=as_factor(Cluster)) %>%
ggplot(aes(x=Cluster, y=Count.class.cluster, fill=Orientation.Class)) +
geom_bar(stat='identity', position='stack') +
theme_bw() +
x.theme.axis.rotate +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
labs(x="Identical Genome Cluster", y="Interaction Count", fill="Orientation Type")`summarise()` has grouped output by 'Cluster'. You can override using the `.groups` argument.p.PHE.identical.Orientation_class.byZerodist.cluster
d.PHE.identical.GenderOrientation.byZerodist.cluster <- left_join(PHE.identicals.network.components[,c("Taxa1","Cluster")], PHE.metadata.linked[,c("Sample_Name","phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], by=c("Taxa1"="Sample_Name")) %>%
dplyr::group_by(TPA.pinecone.sublineage, Cluster, gender_orientation) %>%
dplyr::summarise(count.orient.cluster=n()) %>%
dplyr::mutate(count.cluster=sum(count.orient.cluster), fract=count.orient.cluster/count.cluster) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(count.cluster)) %>%
dplyr::mutate(Cluster.o=as_factor(Cluster))`summarise()` has grouped output by 'TPA.pinecone.sublineage', 'Cluster'. You can override using the `.groups` argument.# Plot sample counts by genome cluster (coloured by orientation)
p.PHE.identical.GenderOrientation.byZerodist.cluster <- d.PHE.identical.GenderOrientation.byZerodist.cluster %>%
ggplot(aes(Cluster.o, count.orient.cluster, fill=gender_orientation)) +
geom_bar(stat="identity", width=0.65) +
scale_fill_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation, guide = guide_legend(order = 1)) +
theme_light() +
x.theme.axis.rotate +
scale_y_continuous(breaks=seq(0,45,5)) +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
labs(x="Identical Genome Cluster", y="Sample Count", fill="Patient Gender Orientation")
# Add details of sublineage
p.PHE.identical.GenderOrientation.byZerodist.cluster <- p.PHE.identical.GenderOrientation.byZerodist.cluster +
ggnewscale::new_scale_color() +
geom_point(data=(d.PHE.identical.GenderOrientation.byZerodist.cluster %>% select(Cluster.o, TPA.pinecone.sublineage) %>% distinct()), aes(Cluster.o, -1.5, color=TPA.pinecone.sublineage), inherit.aes = F) + scale_color_manual(values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage, name="Sublineage", guide = guide_legend(order = 2)) +
NULL
# Add a sublineage axis label (bit of a hack)
p.PHE.identical.GenderOrientation.byZerodist.cluster <- p.PHE.identical.GenderOrientation.byZerodist.cluster +
geom_text(data=data.frame(lab="Sublineage", y=-1.5, x=28, stringsAsFactors=F), aes(label=lab, x=x, y=y), hjust = 0.1, size=theme.text.size.within, inherit.aes = F) +
coord_cartesian(x=c(1, 27), clip='off')
p.PHE.identical.GenderOrientation.byZerodist.cluster
#ggsave(paste0(Figure_output_directory,"SupFig6_Identical-SNP-clust_orientation.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=120, height=100, device='pdf', dpi=1200)
Possible to introduce some more info into that plot?
d.PHE.identical.region.byZerodist.cluster <- left_join(PHE.identicals.network.components[,c("Taxa1","Cluster")], PHE.metadata.linked[,c("Sample_Name","phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], by=c("Taxa1"="Sample_Name")) %>%
dplyr::group_by(TPA.pinecone.sublineage, Cluster, phe_centre) %>%
dplyr::summarise(count.region.cluster=n()) %>%
dplyr::mutate(count.cluster=sum(count.region.cluster), fract=count.region.cluster/count.cluster) %>%
dplyr::ungroup() %>%
dplyr::arrange(desc(count.cluster)) %>%
dplyr::mutate(Cluster.o=as_factor(Cluster))`summarise()` has grouped output by 'TPA.pinecone.sublineage', 'Cluster'. You can override using the `.groups` argument.p.PHE.identical.Region.byZerodist.cluster <- d.PHE.identical.region.byZerodist.cluster %>%
ggplot(aes(Cluster.o, count.region.cluster, fill=phe_centre)) +
geom_bar(stat="identity", width=0.65, position='fill') +
scale_fill_manual(name="UKHSA\nRegion", values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region, guide = guide_legend(order = 1)) +
theme_light() +
x.theme.axis.rotate +
scale_y_continuous(breaks=seq(0,45,5)) +
theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
guides(fill=guide_legend(ncol=2)) +
labs(x="Identical Genome Cluster", y="Region Proportion", fill="UKHSA Region")
PHE.identicals.network.gg.region.scatterpie.groups <- PHE.identicals.network.gg %>%
dplyr::select(Cluster, Taxa1, phe_centre) %>%
dplyr::distinct() %>%
dplyr::group_by(Cluster, phe_centre) %>%
dplyr::summarise(Count.centre=n()) %>%
dplyr::mutate(x=Cluster, y=3.5) %>%
pivot_wider(names_from="phe_centre", values_from="Count.centre", values_fill=0) %>%
dplyr::select(Cluster,x,y,unique(PHE.identicals.network.gg$phe_centre)) %>%
dplyr::ungroup() %>%
dplyr::mutate(Cluster.numeric=as.numeric(1:27))`summarise()` has grouped output by 'Cluster'. You can override using the `.groups` argument.
p.PHE.identical.GenderOrientation.byZerodist.cluster +
ggnewscale::new_scale_fill() #+
NA
NA
Get a few more stats on the largest cluster (Cluster 8)
#d.PHE.identical.GenderOrientation.byZerodist.cluster %>% filter(Cluster=="Cluster8")
PHE.identicals.network.gg.identical.cluster8 <- PHE.identicals.network.gg %>% filter(Cluster=="Cluster8") %>%
select(vertex.names, Orientation.Class, phe_centre, year, TPA_Lineage, TPA.pinecone.sublineage, hivpos, Cluster)
sort(unique(PHE.identicals.network.gg.identical.cluster8$year))[1] 2012 2013 2014 2015 2016 2017 2018
Get some more information about the heterosexual only clusters
PHE.identicals.network.gg.identical_heteroclusters <- PHE.identicals.network.gg %>% filter(Cluster %in% c("Cluster12", "Cluster20", "Cluster27")) %>%
select(vertex.names, Cluster, gender_orientation, phe_centre, year, TPA_Lineage, TPA.pinecone.sublineage, hivpos) %>%
distinct() %>%
arrange(Cluster, year, gender_orientation)
PHE.identicals.network.gg.identical_heteroclustersAnd do the same for the small mixed/GBMSM clusters
PHE.identicals.network.gg.identical_not.heteroclusters <- PHE.identicals.network.gg %>% filter(Cluster %notin% c("Cluster12", "Cluster20", "Cluster27", "Cluster8")) %>%
select(vertex.names, Cluster, gender_orientation, phe_centre, year, TPA_Lineage, TPA.pinecone.sublineage, hivpos) %>%
distinct() %>%
arrange(Cluster, year, gender_orientation)
PHE.identicals.network.gg.identical_not.heteroclustersWhat proportion of heterosexuals have an identical GBMSM paired genome?
# Delineate heterosexual clusters
d.PHE.identical.heterosexual.clusters <- d.PHE.identical.GenderOrientation.byZerodist.cluster %>%
dplyr::mutate(is.heterosexual=ifelse(gender_orientation%in% c("MSW", "WSM"), "heterosexual", ifelse(gender_orientation=="GBMSM","GBMSM", "Unknown"))) %>%
dplyr::group_by(Cluster,is.heterosexual) %>%
dplyr::mutate(count.hetero=sum(count.orient.cluster), fract.hetero=sum(count.orient.cluster)/count.cluster) %>%
dplyr::ungroup() %>%
dplyr::filter(is.heterosexual=="heterosexual") %>%
dplyr::select(-c(count.orient.cluster, gender_orientation, fract)) %>%
dplyr::distinct() %>%
dplyr::mutate(cluster.type=ifelse(fract.hetero==1, "hetero.only", "other"))
d.PHE.identical.heterosexual.clusters
# What proportion of heterosexuals (n=20) are in a heterosexual-only cluster?
d.PHE.identical.heterosexual.clusters %>%
dplyr::group_by(cluster.type) %>%
dplyr::summarise(count.in.hetero.cluster=sum(count.hetero)) %>%
dplyr::mutate(fract.in.hetero=count.in.hetero.cluster/sum(count.in.hetero.cluster))
#left_join(PHE.identicals.network.components[,c("Taxa1","Cluster")], PHE.metadata.linked[,c("Sample_Name","phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], by=c("Taxa1"="Sample_Name"))
Revisions 03-2023 onwards
Look at proportion of genomes at different coverage thresholds
# Cumulative proportion of N counts in genomes
PHE.metadata.Ncount.cummulative.UK <- PHE.metadata.linked %>%
dplyr::filter(is.UK=="UK") %>%
dplyr::group_by(`Proportion-N_>5_mapping+masking_Nichols`) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.Count=sum(Count)) %>%
dplyr::mutate(fraction=Count/total.Count, cum_fract=cumsum(fraction), cum_count=cumsum(Count)) %>%
dplyr::mutate(Dataset="UK (n=237)")
PHE.metadata.Ncount.cummulative.UK
PHE.metadata.Ncount.cummulative.ALL <- TPA.meta2.1 %>%
dplyr::filter(full.temporal.analysis=="Yes") %>%
dplyr::group_by(`Proportion-N_>5_mapping+masking_Nichols`) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.Count=sum(Count)) %>%
dplyr::mutate(fraction=Count/total.Count, cum_fract=cumsum(fraction), cum_count=cumsum(Count)) %>%
dplyr::mutate(Dataset="All (n=520)")
PHE.metadata.Ncount.cummulative.ALLPHE.metadata.Ncount.cummulative.combi <- rbind(PHE.metadata.Ncount.cummulative.UK, PHE.metadata.Ncount.cummulative.ALL)
p.cumulative.Ncount.for.datset <- ggplot(PHE.metadata.Ncount.cummulative.combi , aes(`Proportion-N_>5_mapping+masking_Nichols`, cum_fract, group=Dataset, color=Dataset)) +
geom_point(alpha=0.75, size=1) +
theme_light() +
theme.text.size + theme(legend.position = 'top') +
labs(y="Cumulative fraction of genomes", x="Proportion of sites masked to N") +
scale_y_continuous(breaks=seq(0,1,0.1))
p.cumulative.Ncount.for.datset
BEAST 95% HPD calculations (provide more details for 520 dataset )
BEAST.median <- 1.28e-7
BEAST.95HPD <- c(1.07e-7, 1.48e-7)
SS14.aln.length <- 1139569
1/(BEAST.median * SS14.aln.length)[1] 6.8556621/(BEAST.95HPD * SS14.aln.length)[1] 8.201166 5.929221
Further evaluation of sublineage 6 (reviewer response) using ancestral reconstruction performed on the global TPA-only alignment/tree used in Beale 2021.
TPA.treetime.ancestral.tree <- read.nexus(TPA.treetime.ancestral.tree.file)
TPA.treetime.ancestral.tree.data <- fortify(TPA.treetime.ancestral.tree)
ggtree(TPA.treetime.ancestral.tree) + geom_nodelab(size=2)
# Read in and process TPA-only vcf (to confirm sites are the same)
TPA.only.midpoint.treetime.ancestral.vcf <- read.vcfR(TPA.treetime.ancestral.vcf.file, verbose = FALSE)
TPA.only.midpoint.treetime.ancestral.vcf.fix <- getFIX(TPA.only.midpoint.treetime.ancestral.vcf)
TPA.only.midpoint.treetime.ancestral.vcf.fix <- data.frame(TPA.only.midpoint.treetime.ancestral.vcf.fix[,c(2,4,5)], stringsAsFactors = F)
TPA.only.midpoint.treetime.ancestral.vcf.fix$in.TPA.only <- "yes"
TPA.only.midpoint.treetime.ancestral.vcf.fix$Key <- 1:nrow(TPA.only.midpoint.treetime.ancestral.vcf.fix)
Extract genotype sites
TPA.treetime.ancestral.vcf.gt <- extract_gt_tidy(TPA.only.midpoint.treetime.ancestral.vcf)Extracting gt element GTTPA.treetime.ancestral.vcf.gt.f <- plyr::join(TPA.treetime.ancestral.vcf.gt, TPA.only.midpoint.treetime.ancestral.vcf.fix[,c("Key","POS")], by="Key", type="left")
TPA.treetime.ancestral.vcf.gt.f$POS <- as.numeric(TPA.treetime.ancestral.vcf.gt.f$POS)
TPA.treetime.ancestral.vcf.gt.f$gt_GT <- as.numeric(TPA.treetime.ancestral.vcf.gt.f$gt_GT)
TPA.treetime.ancestral.vcf.gt.f.spread <- tidyr::spread(TPA.treetime.ancestral.vcf.gt.f[,c("POS","Indiv","gt_GT")], POS, gt_GT)
Use snpEff to annotate multi-vcf, and then pull in annotations here
TPA.snpEff <- read.table(TPA.snpEff.file,header = T, check.names = F, comment.char = "",sep="\t")
TPA.snpEff.filt <- TPA.snpEff[!(TPA.snpEff$`ANN[*].GENE`=="gene-TPASS_RS00040" & TPA.snpEff$`ANN[*].EFFECT`=="intragenic_variant"),]
TPA.snpEff.filt[TPA.snpEff.filt$`ANN[*].EFFECT`==".","ANN[*].EFFECT"] <- "intragenic_variant"
TPA.snpEff.filt %>% dplyr::group_by(`ANN[*].EFFECT`) %>% summarise(Count=n())TPA.snpEff.filt %>% dplyr::group_by(`ANN[*].GENE`) %>% summarise(Count=n())TPA.snpEff.filt %>% dplyr::group_by(`ANN[*].GENE`,`ANN[*].EFFECT`) %>% summarise(Count=n())`summarise()` has grouped output by 'ANN[*].GENE'. You can override using the `.groups` argument.TPA.snpEff.filt.var.per.pos <- TPA.snpEff.filt %>% dplyr::group_by(POS) %>% summarise(Count=n())
TPA.snpEff.filt.var.per.pos.multi <- as.numeric(as.character(unlist(TPA.snpEff.filt.var.per.pos[TPA.snpEff.filt.var.per.pos$Count>1,"POS"])))
TPA.snpEff.filt[TPA.snpEff.filt$POS %in% TPA.snpEff.filt.var.per.pos.multi,]NA
Lets pull in gene function (where known) for these sites from the gff
SS14.gff <- ape::read.gff(SS14.gff.file)
SS14.gff.cds <- SS14.gff[SS14.gff$type=="CDS",]
#### function to extract different fields from attributes column
getAttributeField <- function (x, field, attrsep = ";") {
s = strsplit(x, split = attrsep, fixed = TRUE)
sapply(s, function(atts) {
a = strsplit(atts, split = "=", fixed = TRUE)
m = match(field, sapply(a, "[", 1))
if (!is.na(m)) {
rv = a[[m]][2]
}
else {
rv = as.character(NA)
}
return(rv)
})
}
###
#getAttributeField(SS14.gff.cds$attributes, "Name")
# Extract attribute elements from gff
SS14.gff.cds$geneid <- gsub("gene\\-","",getAttributeField(SS14.gff.cds$attributes, "Parent"))
SS14.gff.cds$locus_tag <- getAttributeField(SS14.gff.cds$attributes, "locus_tag")
SS14.gff.cds$gene <- getAttributeField(SS14.gff.cds$attributes, "gene")
SS14.gff.cds$product <- getAttributeField(SS14.gff.cds$attributes, "product")
SS14.gff.cds$proteinid <- getAttributeField(SS14.gff.cds$attributes, "protein_id")
# create a merged locus_tag/gene the way snpEff does
SS14.gff.cds$geneid <- sapply(1:nrow(SS14.gff.cds), function(x) ifelse(is.na(SS14.gff.cds$gene[x]),SS14.gff.cds$locus_tag[x], SS14.gff.cds$gene[x]))
SS14.gff.cds$gene.coords <- paste0(SS14.gff.cds$start,":",SS14.gff.cds$end)
SS14.gff.cds
# read in snp classifications, and apply to discriminatory SNPs
Write this as a function. Takes 4 arguments: - dataframe of snps for each sample in wide matrix format (e.g. TPA.treetime.ancestral.vcf.gt.f.spread) - longform list of SNPs and possible alleles (e.g. TPA.treetime.ancestral.vcf.fix) - variant annotations dataframe (e.g. TPA.snpEff.filt) - a vector of two nodes in the tree to compare (e.g. tt.nodes.to.compare.SS14)
extract_branch_site_allelic_functions <- function(allele.matrix.spread, snp.table, snp.annotation.table, nodes.list){
# filter SNP matrix to only include the two nodes of interest
discriminatory.sites1 <- allele.matrix.spread[allele.matrix.spread$Indiv %in% nodes.list,]
discriminatory.sites2 <- tidyr::gather(discriminatory.sites1,POS,Gt,-Indiv) %>%
tidyr::spread(Indiv, Gt)
# Filter SNPs under consideration to those that are different between the two nodes
discriminatory.sites2 <- discriminatory.sites2[(discriminatory.sites2[,2]!=discriminatory.sites2[,3]),]
discriminatory.sites2 <- discriminatory.sites2[order(as.numeric(discriminatory.sites2$POS)),]
# merge in the details about alleles at each relevant SNP position
discriminatory.sites2 <- plyr::join(discriminatory.sites2, snp.table,by=c('POS'), type='left')
# deal with multi-allelic sites, and discriminate between them
discriminatory.sites2$ALT.multi <- discriminatory.sites2$ALT
discriminatory.sites2$ALT <- sapply(1:nrow(discriminatory.sites2), function(x) strsplit(discriminatory.sites2$ALT.multi[x],",")[[1]][sort(as.numeric(((discriminatory.sites2[x,c(2,3)]))))[2]])
# merge in the annotation for the appropriate allele/SNPs
discriminatory.sites2.snpeff <- plyr::join(snp.annotation.table[,c("POS","ALT","ANN[*].ALLELE","ANN[*].EFFECT","ANN[*].GENE","ANN[*].HGVS_C","ANN[*].HGVS_P")], discriminatory.sites2[,c("POS","REF","ALT",nodes.list)], type="right", by=c("POS","ALT"))
discriminatory.sites2.snpeff[is.na(discriminatory.sites2.snpeff$`ANN[*].EFFECT`),"ANN[*].EFFECT"] <- "intragenic_variant"
# return output
return(discriminatory.sites2.snpeff)
}
#tt.nodes.to.compare.SS14.vs.Nichols.TPA <- c("NODE_0000005","NODE_0000103")
#tt.nodes.to.compare.sublineage6.vs.MRCA.TPA <- c("NODE_0000003","NODE_0000002")
tt.nodes.to.compare.sublineage6.vs.MRCA.TPA <- c("NODE_0000001","NODE_0000002")
sublin6.vs.mrca.Nichols.branch_site_alleles.TPA <- extract_branch_site_allelic_functions(TPA.treetime.ancestral.vcf.gt.f.spread,TPA.only.midpoint.treetime.ancestral.vcf.fix,TPA.snpEff.filt, tt.nodes.to.compare.sublineage6.vs.MRCA.TPA)
sublin6.vs.mrca.Nichols.branch_site_alleles.TPA %>% dplyr::group_by(`ANN[*].EFFECT`) %>% dplyr::summarise(count=n())
paste0("All Variants: ", nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA))[1] "All Variants: 51"paste0("Unique Sites: ", length(unique(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$POS)))[1] "Unique Sites: 51"paste0("Synonymous Variants: ", nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA[sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$`ANN[*].EFFECT`=="synonymous_variant",]))[1] "Synonymous Variants: 11"paste0("Non-Synonymous Variants: ", nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA[sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$`ANN[*].EFFECT`=="missense_variant",]))[1] "Non-Synonymous Variants: 36"paste0("Intragenic Variants :", nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA[sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$`ANN[*].EFFECT`=="intragenic_variant",]))[1] "Intragenic Variants :4"sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var <- c(0, sapply(2:nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA) , function(x) as.numeric(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$POS[x]) - as.numeric(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$POS[x-1])))
mean(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var)[1] 22075.57median(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var)[1] 15611min(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var)[1] 0max(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var) [1] 106334p.sublineage6.ancestral.SNPs.genomepos <- ggplot(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA, aes(x=as.numeric(POS), y=dist.from.last.var)) +
geom_point(size=1, alpha=0.5) +
#geom_bar(stat='identity', alpha=0.5) +
#geom_line(alpha=0.1) +
theme_light() + theme(text = element_text(size = 10)) +
coord_cartesian(xlim=c(0,SS14.aln.length)) +
scale_x_continuous(breaks=pretty) +
scale_y_log10() +
labs(x="SS14 Genome Position (NC_021508.1; (bp))", y="Distance of variant from previous variant site (bp)", title="Genome position of SNPs delineating Sublineage 6 from MRCA node")
p.sublineage6.ancestral.SNPs.genomeposWarning: Transformation introduced infinite values in continuous y-axis
p.sublineage6.ancestral.SNPs.dist.between.histo <- sublin6.vs.mrca.Nichols.branch_site_alleles.TPA %>%
ggplot(aes(x=dist.from.last.var)) +
scale_x_log10() +
geom_histogram(bins=50) +
theme_light() + theme(text = element_text(size = 10)) +
labs(x="Distance of variant from previous variant site (bp)", y="Count") + coord_flip()
p.sublineage6.ancestral.SNPs.dist.between.histoWarning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 1 rows containing non-finite values (stat_bin).
plot_grid(p.sublineage6.ancestral.SNPs.genomepos, p.sublineage6.ancestral.SNPs.dist.between.histo + y.theme.strip , rel_widths = c(8,1), align = T)Warning: Transformation introduced infinite values in continuous y-axis
Warning: Transformation introduced infinite values in continuous x-axis
Warning: Removed 1 rows containing non-finite values (stat_bin).
Do some further analysis of the North East sublineage distributions. We have 35 samples collected from these regions, of which 17 were collected from 2014 onwards. Is sublineage 14 missing by chance (could we be missing it simply because we haven’t collected enough samples) or is this more likely to reflect true uneven regional distributions?
# How many genomes found in Northern regions before and after first detection of sublineage 14 in 2014?
PHE.metadata.linked %>%
dplyr::mutate(before2014=ifelse(year>=2014,"2014onwards", "pre2014")) %>%
dplyr::filter(phe_centre %in% c("North East", "North West", "Yorkshire and Humber")) %>%
dplyr::group_by(before2014) %>%
dplyr::summarise(count=n())
# What are the proportions of different sublineages around the UK before and after 2014?
PHE.meta.post2014.sublin.fracs <- PHE.metadata.linked %>%
#dplyr::filter(year>=2014) %>%
dplyr::mutate(before2014=ifelse(year>=2014,"2014onwards", "pre2014")) %>%
dplyr::group_by(before2014, TPA.pinecone.sublineage) %>%
dplyr::summarise(Count=n()) %>%
dplyr::mutate(total.all=sum(Count)) %>%
dplyr::mutate(fraction=Count/total.all) %>%
dplyr::arrange(desc(TPA.pinecone.sublineage), .by_group=T) %>%
dplyr::mutate(cum_fract = cumsum(fraction)) %>%
dplyr::mutate(cum_fract.mid = cum_fract-(fraction/2)) %>%
dplyr::mutate(Lineage.perc=(Count/sum(Count)*100))`summarise()` has grouped output by 'before2014'. You can override using the `.groups` argument.PHE.meta.post2014.sublin.fracs
# simulating poisson process r to work out how many samples we would expect in Northern England under poisson distribution
# What % of sublineage 14 samples are found in the total population?
post2014.sublin14.freq <- PHE.meta.post2014.sublin.fracs %>% filter(before2014=="2014onwards", TPA.pinecone.sublineage==14) %>% select(Lineage.perc) %>% pull()Adding missing grouping variables: `before2014`
# Simulate and plot a Poisson distribution of how many sublineage 14 samples we would expect to find if we randomly selected 17 samples at 22%
data.frame(rpois=rpois(1000000, 17/(100/post2014.sublin14.freq))) %>%
ggplot(aes(rpois)) + geom_histogram(binwidth=1) +
scale_x_continuous(breaks=seq(0,20,2)) +
theme_light() +
labs(x="Samples Found", y="Simulation Count")
# What are the quantile distributions from that?
quantile(rpois(1000000, 17/(100/post2014.sublin14.freq)), probs=c(0.01, 0.05, 0.5, 0.95, 0.99)) 1% 5% 50% 95% 99%
0 1 4 7 9 median(rpois(1000000, 17/(100/post2014.sublin14.freq)))[1] 4mean(rpois(1000000, 17/(100/post2014.sublin14.freq)))[1] 3.798339# What is the probability of finding no samples (assuming uniform unbiased coverage)?
data.frame(n=seq(0,20,1), dpois=sapply(seq(0,20,1), function(x) dpois(x, lambda=17/(100/post2014.sublin14.freq)))) %>%
ggplot(aes(x=n, y=dpois)) +
geom_bar(stat='identity') +
scale_x_continuous(breaks=pretty) +
theme_light() +
labs(x="Samples Found", y="Probability")
paste("Probability of finding zero samples is ", round(dpois(0, lambda=17/(100/post2014.sublin14.freq)), 5)) [1] "Probability of finding zero samples is 0.02244"---
title: "R Notebook - Treponema UK UKHSA-cohort Analysis 2022. Revision 04-2023"
output:
  pdf_document: default
  html_notebook: default
  word_document: default
---

Make a clean environment
```{r}
 rm(list=ls())
```
\
Load packages
```{r}
packages.list <- c("ggplot2","treeio","ggtree","ggnewscale","ape","dplyr","tidyverse","tidyr","phytools","RColorBrewer","lubridate","readxl","ggforce","ggstance","ggridges","cowplot","hexbin","scales","haven","network","ggnetwork","intergraph","igraph","ggraph","graphlayouts","scatterpie","maps","mapdata","maptools","rgdal","rgeos","broom","ggrepel","ggridges","magick","ggbeeswarm","ggrastr")

#"plyr","Cairo","ggmap","emojifont","rPinecone","pairsnp","CoordinateCleaner","gridExtra","dendextend","ggdendro",

#BiocManager::install("ggtree")
#BiocManager::install("treeio")

for(pkg in packages.list){
  eval(bquote(library(.(pkg)))) }
```
\
Confirm current environmental setup
```{r}
R.Version()
print(sessionInfo())
```
\
Make some shortcuts for plotting 
```{r}
y.theme.strip <- theme(axis.title.y = element_blank(), axis.text.y = element_blank(), axis.ticks.y= element_blank())
y.theme.strip.partial <- theme(axis.text.y = element_blank(), axis.ticks.y= element_blank())

x.theme.strip <- theme(axis.title.x = element_blank(), axis.text.x = element_blank(), axis.ticks.x= element_blank())
x.theme.strip.partial <- theme(axis.text.x = element_blank(), axis.ticks.x= element_blank())
x.theme.strip.labs <- theme(axis.text.x = element_blank(),axis.title.x = element_blank())

x.theme.axis.rotate <- theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))

legend.strip <- theme(legend.position = "none")

theme.text.size <- theme(text = element_text(size = 10))

'%notin%' <- Negate('%in%')

max.font.size <- 7
basic.font.size <- 6
min.font.size <- 5.25
theme.text.size <- theme(text = element_text(size = basic.font.size))
theme.text.size.within <- (5/14)*min.font.size
panel.lab.size <- 10

```
\
Specify raw data - global dataset
```{r}
#Data_input_directory <- "/Users/mb29/Papers/Treponema_UK-PHE-gen-epi_2021/Data/"
#Data_input_directory <- "/Users/mb29/Papers/Treponema_UK-PHE-gen-epi_2021/Rnotebook/Rnotebook_09-2022/data/"
Data_input_directory <- paste0(getwd(), "/inputdata/")


################################
#### Tree data 

# ML tree (refined dataset)
TPA.MLtree.file <- paste0(Data_input_directory,"TPA-uber.remasked.2020-11-10.goodcov25.gubbins.SNPs.aln.renamed.fix-zero-dist.treefile")

# Pyjar tree (refined dataset)
TPA.pyjar.file <- paste0(Data_input_directory,"TPA-uber.remasked.2020-11-10.goodcov25.gubbins.SNPs.aln.renamed.pyjar.tre")

# Full size BEAST2 analysis - previously generated as part of Beale, 2021.
full.beast2.tree.file <- paste0(Data_input_directory,"TPA-uber_beast2_strict-skyline-500M_10pop_consensus.tree")

# Ancestral reconstruction of global TPA ML tree from TreeTime (refined dataset)
TPA.treetime.ancestral.tree.file <- paste0(Data_input_directory,"TPA.annotated_tree.fix-hung.nexus")
TPA.treetime.ancestral.vcf.file <- paste0(Data_input_directory,"TPA-uber.midpoint.ancestral_sequences.fix-hung.vcf")
# Functionally annotated variants, extracted from snpEff vcf into tsv using snpSift
TPA.snpEff.file <- paste0(Data_input_directory,"TPA-uber.midpoint.ancestral_sequences.relab.bcf.ann.vcf.vartab.sepline.tsv")
# Gff file for SS14 reference genome, containing gene positions/annotations
SS14.gff.file <- paste0(Data_input_directory,"Treponema_pallidum_subs._pallidum_SS14.NC_021508.1.2021-06-13.gff")

################################
#### Meta data 

# Supplement from TPA-Uber paper - Beale, 2021 
TPA.meta2.file <- paste0(Data_input_directory,"Sup_Data1_Global_Sample-Metadata__09-2022.xlsx")

# England specific metadata collated by PHE/UKHSA
PHE.metadata.linked.file <- paste0(Data_input_directory,"Sup_Data2_TPA.UK-only.PHE.metadata.2022-02-02.xlsx")

# England specific mapping shapefile data with Public Health Boundaries
# Imported datafile from https://geoportal.statistics.gov.uk/datasets/public-health-england-centres-december-2016-full-clipped-boundaries-in-england/explore?location=52.950000%2C-2.000000%2C6.88
UK.publichealth.shapefile.data <- paste0(Data_input_directory,"Public_Health_England_Centres_(December_2016)_Boundaries")


################################
#### Externally plotted figures (e.g. GrapeTree) for inclusion in multipanel figures

# Externally plotted grapetree minimum spanning tree for whole of England - code to extract subtree that was used to make this is included later in this Rnotebook
TPA.UK.Grapetree.sublineages.file <- paste0(Data_input_directory,"TPA-UK-2022-02-03.sublineage-MSTree.Inkscaped.svg")

# Externally plotted grapetree minimum spanning tree for whole of England - 3-variable plots
TPA.UK.Grapetree.3way.file <- paste0(Data_input_directory,"TPA-UK-2022-02-16.-MSTree_3-way-figure.Inscaped-3.svg")

# Externally plotted grapetree minimum spanning tree for whole of England - HIV status
TPA.UK.Grapetree.HIV.file <- paste0(Data_input_directory,"TPA-UK-2022-02-03.HIVstatus-MSTree_inkscaped.svg")

# Externally plotted grapetree minimum spanning tree for North East England networks
TPA.NorthEastEngland.Grapetree.file <- paste0(Data_input_directory,"TPA-UK-NorthEast-2022-02-26.GenderOrientation-MSTree.inkscaped.+node-counts+GBMSM.svg")


```
\
Specify directory to output plots
```{r}
Figure_output_directory <- paste0(getwd(), "/Figures_revision_03-2023/")

#"/Users/mb29/Papers/Treponema_UK-PHE-gen-epi_2021/Figures/Figure_Drafting/Working_Figures_08-2022/"
```
\
Read in trees
```{r}
TPA.MLtree <- midpoint.root(read.tree(TPA.MLtree.file))
TPA.pyjar.tree <- midpoint.root(read.tree(TPA.pyjar.file))
```
\
Read in final output metadata from Global Uber study (Beale 2021)
```{r}
TPA.meta2.1 <- readxl::read_excel(TPA.meta2.file,sheet="Supplementary_Data1_Sample-Meta")
```
\
Create a colour scheme for Lineages, Countries and Continents (consistent with Beale, 2021)
```{r}
# Colouring for country
continental.country.cols.brew2 <- unique(TPA.meta2.1[,c("Geo_Country","Continent")])
continental.country.cols.brew2 <- continental.country.cols.brew2[order(continental.country.cols.brew2$Continent,continental.country.cols.brew2$Geo_Country),]

continental.country.cols.brew2$country.col <- c("#ec7014","#fec44f","#de2d26","#fb6a4a","#bdbdbd","#737373",brewer.pal(n=8,"Purples")[4:8],brewer.pal(n=8,"Blues")[3:8],brewer.pal(n=5,"Greens")[3:5],"#c51b8a","#8c510a")

# Colouring for Continent
continental.cols.brew2 <- data.frame(Continent=sort(unique(TPA.meta2.1$Continent)),stringsAsFactors=F)
continental.cols.brew2$continent.col <- c("#fec44f","#de2d26","#bdbdbd","#2171b5","#74c476","#c51b8a","#ec7014")


# Colouring for TPA Lineage
TPA_Lineage.cols <- data.frame(Lineage=sort(unique(TPA.meta2.1$TPA_Lineage)),stringsAsFactors=F)
TPA_Lineage.cols$Lineage.col <- c("royalblue2", "indianred1")
#c("#436eee", "#666666","#ff6a6a")
TPA_Lineage.cols$Lineage <- factor(TPA_Lineage.cols$Lineage, levels=c("Nichols","SS14","outlier"))

# Lineage Hexcodes
# royalblue2 #436eee
# indianred1 #ff6a6a
```
\
Define colours for sublineages
```{r}
# Define sublineage clustering scheme using brew colourscales
sublineages.cols.brew <- data.frame(unique(TPA.meta2.1[,c("TPA_Lineage","TPA.pinecone.sublineage")]), stringsAsFactors = F)
sublineages.cols.brew <- sublineages.cols.brew[order(sublineages.cols.brew$TPA_Lineage,sublineages.cols.brew$TPA.pinecone.sublineage),]

sublineages.cols.brew$sublin.order <- as.numeric(as.character(sublineages.cols.brew$TPA.pinecone.sublineage))
sublineages.cols.brew <- sublineages.cols.brew[order(sublineages.cols.brew$sublin.order),]

# For revised bootstrapped clusters
sublineages.cols.brew$sublineage.cols <- c("#FC9272","#EF3B2C",brewer.pal(n=4,"Greens")[2:4],brewer.pal(n=4,"YlOrBr")[c(2,3)],brewer.pal(n=6,"Blues")[2:6],brewer.pal(n=6,"Purples")[2:6],"grey80","grey80","grey80","grey80")
  
sublineages.cols.brew <- unique(sublineages.cols.brew[,c("TPA.pinecone.sublineage","sublineage.cols")])
sublineages.cols.brew <- sublineages.cols.brew[order(as.numeric(as.character(sublineages.cols.brew$TPA.pinecone.sublineage))),]
sublineages.cols.brew$TPA.pinecone.sublineage <- factor(sublineages.cols.brew$TPA.pinecone.sublineage, levels=sublineages.cols.brew$TPA.pinecone.sublineage)
sublineages.cols.brew <- sublineages.cols.brew[!is.na(sublineages.cols.brew$sublineage),]

colnames(sublineages.cols.brew) <- c("sublineage","sublineage.cols")
sublineages.cols.brew <- unique(sublineages.cols.brew)
```
\
Restrict analysis to high quality genomes (and tree)
```{r}
TPA.meta2.1 <- TPA.meta2.1[TPA.meta2.1$finescale.analysis=="Yes",]
```
\
Create a "UK" variable, and a "PHE" variable
```{r}
TPA.meta2.1$is.UK <- ifelse(TPA.meta2.1$Geo_Country=="UK","UK","Other")
TPA.meta2.1$is.PHE <- ifelse(TPA.meta2.1$Geo_Country=="UK" & grepl("PHE",TPA.meta2.1$Sample_Name),"PHE","Other")
```
\
```{r}
# Prepare ML tree
TPA.MLtree.ggtree <- ggtree(TPA.MLtree,layout = "fan",open.angle = 10, right=T)

# Prepare country dataset
TPA.rawseq.countries.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Country=TPA.meta2.1$Geo_Country, stringsAsFactors = F)

# Prepare continent dataset
TPA.rawseq.continents.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Continent=TPA.meta2.1$Continent, stringsAsFactors = F)

# Prepare UK data strip
TPA.rawseq.UK.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, England=TPA.meta2.1$is.UK, stringsAsFactors = F)
TPA.rawseq.UK.p[TPA.rawseq.UK.p$England=="UK",] <- "England"

# Prepare PHE data strip
TPA.rawseq.PHE.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, PHE=TPA.meta2.1$is.PHE, stringsAsFactors = F)

# Prepare Major lineage dataset
TPA.rawseq.Lineage.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Lineage=TPA.meta2.1$TPA_Lineage, stringsAsFactors = F)

# Prepare sublineage lineage dataset
TPA.rawseq.subLineage.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Sublineage=TPA.meta2.1$TPA.pinecone.sublineage, stringsAsFactors = F)


# Prepare Year dataset (all samples)
TPA.rawseq.all.Years.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Year=TPA.meta2.1$Sample_Year, stringsAsFactors = F)


floor_5years  <- function(value){ return(value - value %% 5) }
TPA.meta2.1$Sample_5year.window <- paste0(floor_5years(as.numeric(TPA.meta2.1$Sample_Year)),"-",floor_5years(as.numeric(TPA.meta2.1$Sample_Year))+5)
# Some samples have uncertain dates (up to 20-30 years uncertainty), but for the purposes of these plotting categories we'll use the centrepoint year
TPA.meta2.1$Sample_5year.window <- sapply(1:nrow(TPA.meta2.1), function(x) ifelse(TPA.meta2.1$Sample_Year[x]=="-",NA, ifelse(is.na(TPA.meta2.1$Sample_5year.window[x]),NA, ifelse(TPA.meta2.1$Sample_Year[x]=="1950-1980","1965-1970",ifelse(TPA.meta2.1$Sample_Year[x]=="1960-1980","1965-1970" ,ifelse(TPA.meta2.1$Sample_Year[x]=="1980-1999","1985-1990",TPA.meta2.1$Sample_5year.window[x]))))))


TPA.meta2.1$Sample_year.1990.cuttoff <- ifelse(TPA.meta2.1$Sample_Year>1990,TPA.meta2.1$Sample_Year,"<1990")

TPA.meta2.1$Sample_year.1999.cuttoff <- ifelse(TPA.meta2.1$Sample_Year>1999,TPA.meta2.1$Sample_Year,"<1999")
TPA.rawseq.year.cuttoff.p <- data.frame(row.names=TPA.meta2.1$Sample_Name, Sample.Year=TPA.meta2.1$Sample_year.1999.cuttoff, stringsAsFactors = F)

```
\
\
# Bring in PHE metadata
```{r}
PHE.metadata.linked <- readxl::read_excel(PHE.metadata.linked.file)
```
\
Do some cleanup and factoring of variables
```{r}

PHE.metadata.linked$age_group <- factor(PHE.metadata.linked$age_group, levels=rev(c("16-24","25-34","35-44","45+","Unknown")))

PHE.metadata.linked$london <- factor(PHE.metadata.linked$london,levels=rev(c("Yes","No","Unknown")))
PHE.metadata.linked$ukborn <- factor(PHE.metadata.linked$ukborn,levels=rev(c("Yes","No","Unknown")))
PHE.metadata.linked$hivpos <- factor(PHE.metadata.linked$hivpos, levels=rev(c("Yes","No","Unknown")))

# need to update terminology of 'MSM' to 'GBMSM'
PHE.metadata.linked[PHE.metadata.linked$gender_orientation=="MSM","gender_orientation"] <- "GBMSM"
PHE.metadata.linked$gender_orientation <- factor(PHE.metadata.linked$gender_orientation, levels=rev(c("MSW","GBMSM","WSM","MUnknown","Unknown")))

PHE.metadata.linked$phe_centre <- factor(PHE.metadata.linked$phe_centre, levels=rev(c("East Midlands", "East of England", "London", "North East", "North West", "South East", "South West", "West Midlands", "Yorkshire and Humber", "UK (not England)", "Not Known")))

PHE.metadata.linked$TPA.pinecone.sublineage <-  factor(PHE.metadata.linked$TPA.pinecone.sublineage, levels=sublineages.cols.brew$sublineage)

```
\
\
### Extract information about duplicates
```{r}
PHE.metadata.duplicates <- PHE.metadata.linked[!is.na(PHE.metadata.linked$dup_flag),]
PHE.metadata.duplicates <- PHE.metadata.duplicates[!is.na(PHE.metadata.duplicates$Sample_Name),]


PHE.patient.matches <- data.frame(
    stringsAsFactors = FALSE,
                                   dup_flag = c("1A","1B",
                                                "2A","2B","3A","3B","4A",
                                                "4B","5A","5B"),
                                dup_Patient = c("Patient 1",
                                                "Patient 1","Patient 2",
                                                "Patient 2","Patient 3","Patient 3",
                                                "Patient 4","Patient 4",
                                                "Patient 5","Patient 5"),
                         dup_Patient_Sample = c("sample 1",
                                                "sample 2","sample 1",
                                                "sample 2","sample 1","sample 2",
                                                "sample 1","sample 2","sample 1",
                                                "sample 2")
                       )
                       

PHE.metadata.duplicates <- left_join(PHE.metadata.duplicates, PHE.patient.matches, by="dup_flag")

PHE.metadata.duplicates
```

Duplicate Samples missing metadata are all 'new duplicates' and were excluded due to low mapping coverage (all checked).
\
Samples labelled 'ZA' and 'XB' had duplicates in the original dataset, but the reciprocal pairs were excluded due to quality isues.
\
Available pairs - Patient 3, Patient 4

```{r}
PHE.metadata.duplicates.paired <- PHE.metadata.duplicates[PHE.metadata.duplicates$dup_Patient %in% c("Patient 3","Patient 4"),]
PHE.metadata.duplicates.paired[order(PHE.metadata.duplicates.paired$dup_Patient, PHE.metadata.duplicates.paired$year,PHE.metadata.duplicates.paired$month),c("Sample_Name","dup_Patient", "month.fix", "year")]
```
\
These will be revisited later in the analysis. 
\
Patient 4
HIV-ve MSM (45+), UK born, PHE region D
2 samples, collected in the same month and year
Both samples are sublineage 1, and identical (0 pwSNPs)
Likely the same infection (depending on dates, treatment, etc), but can’t rule out reinfection with same strain.
\
Patient 3
HIV-ve MSM (35-44), not UK born, based in London (C)
2 samples, collected 9 months apart
Both samples are sublineage 1, but have 7 pairwise SNPs between them (loads!)
Reinfection – probably from a different transmission network
\
\
However, based on the sample dates, as well as the outcome of the downstream genetic analysis, we can see that Patient 3 has duplicate infection events (different dates, 10 months apart) and the genomes are distinct (7 SNPs apart), whereas Patient 4 samples were collected in the same month and year (i.e. are likely duplicates from the same infection) and has identical genomes.
\
For downstream analysis purposes, we will retain both samples for Patient 3 (discrete infections), but exclude one sample from Patient 4 (duplicate infection samples) - 'PHE150126A' has much better genome coverage, so exclude 'PHE150125A'
\
\
### Further Exclusions \
PHE130056A - duplicate of PHE130057B (already removed, so not relevant) - don't exclude!
PHE170402A - quality control sample
PHE170378A - quality control sample

\
Exclude duplicate sequences
```{r}
duplicate.exclusion.list <- c("PHE150125A","PHE170402A","PHE170378A")
PHE.metadata.linked <- PHE.metadata.linked[PHE.metadata.linked$Sample_Name %notin% duplicate.exclusion.list,]
```
\

### Moving on... \

Define some colour schemes
```{r}
# define some colors for each region
PHE.region.cols.brew <- data.frame(UKHSA.region=c("North East", "North West", "Yorkshire and Humber", "East Midlands", "West Midlands", "East of England", "London", "South East","South West","UK (not England)", "Not Known"), stringsAsFactors=F)
PHE.region.cols.brew$region.col <- c("#A6CEE3","#1F78B4","#CAB2D6","#33A02C","#B2DF8A","#FF7F00","#E31A1C","#FB9A99","#D4BB02","grey75","grey25")

# HIV color scheme
PHE.hiv.cols <- data.frame(hivpos=rev(sort(unique(PHE.metadata.linked$hivpos))), stringsAsFactors=F)
PHE.hiv.cols$hiv.cols <- c("#1f78b4","#b2df8a","grey75")

# Orientation colour scheme
PHE.orientation.cols <- data.frame(orientation=rev(sort(unique(PHE.metadata.linked$gender_orientation))), stringsAsFactors=F)
PHE.orientation.cols$orientation <- factor(PHE.orientation.cols$orientation, levels=rev(sort(unique(PHE.metadata.linked$gender_orientation))), labels=c("MSW","GBMSM","WSM","MUnknown","Unknown"))
PHE.orientation.cols$orientation.cols <- c("#1f78b4","#b2df8a","#fb9a99","#a6cee3","grey75")

# UK born colour scheme
PHE.ukborn.cols <- data.frame(ukborn=rev(sort(unique(PHE.metadata.linked$ukborn))),ukborn.cols=c("#1f78b4","#b2df8a","grey75"),stringsAsFactors = F)

# London based colour scheme
PHE.london.cols <- data.frame(london=rev(sort(unique(PHE.metadata.linked$london))),london.cols=c("#1f78b4","#b2df8a","grey75"),stringsAsFactors = F)


# Age group colour scheme
PHE.Age.cols <- data.frame(age_group=rev(sort(unique(PHE.metadata.linked$age_group))),stringsAsFactors = T)
PHE.Age.cols$age_group.cols <- c(brewer.pal(n=4,"YlGnBu"),"grey75")

# Sample Date colour scheme
PHE.year.cols <- data.frame(year=(sort(unique(PHE.metadata.linked$year))),stringsAsFactors = T)
PHE.year.cols$year.cols <- brewer.pal(n=7,"YlOrRd")

# Sample Date (all global data, but with 1990 cuttoff)
TPA.year.cuttoff.cols <- data.frame(date.cuttoff=c("<1999",1999:2019), date.cuttoff.col=c("#F2F2F2",colorRampPalette(brewer.pal(7, "YlOrRd"))(length(1999:2019))))


```
\
\
#####
## First describe the sequenced population as a whole
\
Set order of PHE regions
```{r}
PHE.metadata.linked$phe_centre <- factor(PHE.metadata.linked$phe_centre, levels=rev(PHE.region.cols.brew$UKHSA.region))
```
\
Generate some basic statistics about geographical PHE regions (anonymised)
```{r}
PHE.count.all <- PHE.metadata.linked %>% 
  dplyr::summarise(count.per.region=n())

PHE.count.years <- PHE.metadata.linked %>% 
  dplyr::group_by(year) %>%
  dplyr::summarise(count.per.year=n()) %>%
  ungroup() %>%
  dplyr::mutate(perc.per.year=(count.per.year/sum(count.per.year))*100)

# Generate some stats about HIV status
PHE.HIV.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(hivpos) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::mutate(fraction=Count/total.region) %>%
  dplyr::arrange(desc(hivpos), .by_group=T) %>%
  dplyr::mutate(cum_fract = cumsum(fraction)) %>%
  dplyr::mutate(cum_fract.mid = cum_fract-(fraction/2)) %>%
  dplyr::mutate(HIV.perc=(Count/sum(Count)*100))
  
# Generate some stats about gender orientation
PHE.orientation.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(gender_orientation) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(gender_orientation), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>% 
  dplyr::mutate(orientation.perc=(Count/sum(Count)*100))

# Generate some stats about UK born (vague category that's unfortunately only marginally helpful)
PHE.UKborn.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(ukborn) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(ukborn), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>% 
  dplyr::mutate(UKborn.perc=(Count/sum(Count)*100))
  
# Generate some stats about London based
PHE.London.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(london) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(london), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
  dplyr::mutate(London.perc=(Count/sum(Count)*100))

# Generate some stats about Age group
PHE.Age.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(age_group) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(age_group), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
  dplyr::mutate(Age.perc=(Count/sum(Count)*100))

# Generate some stats about Lineage group
PHE.Lineage.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA_Lineage) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(TPA_Lineage), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
  dplyr::mutate(Lineage.perc=(Count/sum(Count)*100))

# Generate some stats about sublineage group
PHE.sublineage.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(TPA.pinecone.sublineage), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>%
  dplyr::mutate(Sublineage.perc=(Count/sum(Count)*100))
```
\
Make some plots
```{r, fig.width=10, fig.height=8}
# Make hbar plot of sample counts by region
p.all.hbarplot <- ggplot(PHE.count.all, aes(x=count.per.region,y="")) +
  geom_barh(stat="identity", position="stack", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
  scale_fill_manual(values="grey30") + 
  geom_text(data=PHE.count.all, aes((count.per.region+12), "",label=count.per.region), size=theme.text.size.within, inherit.aes = F) +
  labs(y="All", x="Sample Count") +
  coord_cartesian(xlim=c(0,260)) +
  guides(fill=guide_legend(nrow=4)) 
#p.all.hbarplot

# make temporal bubbleplot of counts by region
p.all.year.bubbleplot <- ggplot(PHE.count.years, aes(as.numeric(year), y="All")) +
  geom_point(alpha=0.65, aes(size=count.per.year)) + 
  geom_line(alpha=0.25) +
  guides(colour='none') +
  scale_size_area(max_size = 7,breaks=c(1,5,10,25,50)) + 
  guides(size=guide_legend(nrow=2)) +
  theme_light() +
  scale_fill_manual(values="grey30") + 
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
  labs(y="", x="Sample Year", size="Count") 
#p.all.year.bubbleplot

# Make proportional hbar plot of HIV status
p.all.hiv.hbarplot <- ggplot(PHE.HIV.counts, aes(Count,y="",fill=hivpos)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
  scale_fill_manual(name="HIV +ve",values=PHE.hiv.cols$hiv.cols, breaks=PHE.hiv.cols$hivpos) +
  labs(y="All", x="HIV +ve") +
  guides(fill=guide_legend(nrow=3)) +
  geom_text(data=PHE.HIV.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F) +
  NULL
#p.all.hiv.hbarplot

p.all.orientation.hbarplot <- ggplot(PHE.orientation.counts, aes(Count,y="",fill=gender_orientation)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
  scale_fill_manual(name="Orientation",values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) +
  labs(y="All", x="Orientation") +
  guides(fill=guide_legend(nrow=3)) +
  geom_text(data=PHE.orientation.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F)
#p.all.orientation.hbarplot

p.all.ukborn.hbarplot <- ggplot(PHE.UKborn.counts, aes(Count,y="",fill=ukborn)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
  scale_fill_manual(name="UK\nBorn",values=PHE.ukborn.cols$ukborn.cols, breaks=PHE.ukborn.cols$ukborn) +
  labs(y="All", x="UK Born") +
  #guides(fill=guide_legend(nrow=3)) +
  geom_text(data=PHE.UKborn.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F)
#p.all.ukborn.hbarplot

p.all.London.hbarplot <- ggplot(PHE.London.counts, aes(Count,y="",fill=london)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
  scale_fill_manual(name="London",values=PHE.london.cols$london.cols, breaks=PHE.london.cols$london) +
  labs(y="All", x="London") +
  guides(fill=guide_legend(nrow=3)) +
  geom_text(data=PHE.London.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F)
#p.all.London.hbarplot

p.all.Age.hbarplot <- ggplot(PHE.Age.counts, aes(Count,y="",fill=age_group)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='none') +
  scale_fill_manual(name="Age\nGroup",values=PHE.Age.cols$age_group.cols, breaks=PHE.Age.cols$age_group) +
  labs(y="All", x="Age Group") +
  guides(fill=guide_legend(nrow=3)) +
  geom_text(data=PHE.Age.counts, aes(cum_fract.mid, y="",label=Count), size=theme.text.size.within, inherit.aes = F)
#p.all.Age.hbarplot
```
\
Plot combined plot for 'all samples'
```{r, fig.width=12, fig.height=10}
PHE.all.combiplot.1 <- plot_grid(p.all.year.bubbleplot, p.all.hbarplot + y.theme.strip, p.all.orientation.hbarplot + y.theme.strip, p.all.hiv.hbarplot + y.theme.strip, p.all.Age.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(4,2,2,2,2), scale=0.9)

PHE.all.combiplot.1
```
\
\
Next just describe population distributions by PHE region
```{r}
# generate some basic statistics about geographical PHE regions (anonymised)
PHE.geo.count <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre) %>%
  dplyr::summarise(count.per.region=n()) %>%
  dplyr::mutate(total.count=sum(count.per.region),fraction=count.per.region/total.count)

PHE.geo.count.years <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre,year) %>%
  dplyr::summarise(count.per.region.year=n())

PHE.geo.count.years.lineage <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre,year,TPA_Lineage) %>%
  dplyr::summarise(count.per.region.year=n()) %>%
  dplyr::mutate(total.count.year=sum(count.per.region.year)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from=TPA_Lineage, values_from = count.per.region.year)
PHE.geo.count.years.lineage[is.na(PHE.geo.count.years.lineage)] <- 0
PHE.geo.count.years.lineage$year <- as.numeric(PHE.geo.count.years.lineage$year)

# Generate some stats about HIV status
PHE.geo.HIV.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre,hivpos) %>%
  dplyr::summarise(count.per.region.hiv=n()) %>%
  dplyr::mutate(total.region=sum(count.per.region.hiv)) %>%
  dplyr::mutate(fraction=count.per.region.hiv/total.region) %>%
  dplyr::arrange(desc(hivpos), .by_group=T) %>%
  dplyr::mutate(cum_fract = cumsum(fraction)) %>%
  dplyr::mutate(cum_fract.mid = cum_fract-(fraction/2))

# Double Check HIV status data for non-PHE dataset - confirmed no HIV+ves from non-MSM. 
PHE.sourcelab.HIV.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(is.PHE, gender_orientation, hivpos) %>%
  dplyr::summarise(count.per.orientation.hiv=n()) #%>%
  #dplyr::filter(is.PHE!="PHE")

# Get total population stats for HIV
PHE.all.HIV.counts <-  PHE.metadata.linked %>% 
  dplyr::group_by(hivpos) %>%
  dplyr::summarise(count.hiv=n()) %>%
  dplyr::mutate(count.total=sum(count.hiv), fraction=count.hiv/count.total)

  
# Generate some stats about gender orientation
PHE.orientation.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(gender_orientation) %>%
  dplyr::summarise(orientation.count=n()) %>%
  dplyr::mutate(orientation.percent=(orientation.count/sum(orientation.count)*100))

PHE.geo.orientation.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre,gender_orientation) %>%
  dplyr::summarise(count.per.region.orientation=n()) %>%
  dplyr::mutate(total.region=sum(count.per.region.orientation)) %>%
  dplyr::arrange(desc(gender_orientation), .by_group=T) %>%
  dplyr::mutate(fraction=count.per.region.orientation/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) %>% 
  dplyr::mutate(orientation.percent=(count.per.region.orientation/sum(count.per.region.orientation)*100))

# Generate some stats about UK born
PHE.geo.UKborn <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre, ukborn) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(ukborn), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))
  
# Generate some stats about London based
PHE.geo.London <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre, london) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(london), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))

# Generate some stats about Age group
PHE.geo.Age <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre, age_group) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(age_group), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))

# Generate some stats about Lineage group
PHE.geo.Lineage <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre, TPA_Lineage) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(TPA_Lineage), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))

# Generate some stats about sublineage group
PHE.geo.sublineage <- PHE.metadata.linked %>% 
  dplyr::group_by(phe_centre, TPA.pinecone.sublineage) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.region=sum(Count)) %>%
  dplyr::arrange(desc(TPA.pinecone.sublineage), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.region, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))
```
\
Make some plots
```{r, fig.width=10, fig.height=8}
# Make hbar plot of sample counts by region
p.region.hbarplot <- ggplot(PHE.geo.count, aes(count.per.region,phe_centre, fill=phe_centre)) +
  geom_barh(stat="identity", position="stack", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="UKHSA\nRegion",values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region) +
  geom_text(data=PHE.geo.count, aes((count.per.region+12), phe_centre,label=count.per.region), size=theme.text.size.within, inherit.aes = F) +
  labs(y="UKHSA Region", x="Sample Count") +
  #coord_cartesian(xlim=c(0,130)) +
  coord_cartesian(xlim=c(0,260)) +
  guides(fill=guide_legend(ncol=2)) 
#p.region.hbarplot

# make temporal bubbleplot of counts by region
p.region.year.bubbleplot <- ggplot(PHE.geo.count.years, aes(as.numeric(year), phe_centre, colour=phe_centre)) +
  geom_point(alpha=0.65, aes(size=count.per.region.year)) + 
  geom_line(alpha=0.25) +
  guides(colour='none') +
  scale_size_area(max_size = 7,breaks=c(1,5,10,25,50)) + 
  guides(size=guide_legend(nrow=2, direction = 'horizontal', byrow=T)) +
  theme_light() +
  scale_color_manual(name="UKHSA\nRegion",values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region) +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  labs(y="UKHSA Region", x="Sample Year", size="Count") 
#p.region.year.bubbleplot

# Or a barplot of lineage by year & PHE region?
p.region.year.bubbleplot.barplot.facet.lineage <- PHE.geo.count.years.lineage %>% tidyr::pivot_longer(c(SS14, Nichols), names_to="TPA_Lineage", values_to="Count") %>%
  ggplot(aes(year, Count, fill=TPA_Lineage)) + 
  geom_bar(stat='identity', width=0.6) + 
  facet_grid(phe_centre~., scales='free') +
  guides(size=guide_legend(nrow=2)) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="TPA\nLineage",values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage) +
  theme(strip.background = element_rect(color='white', fill='white',linetype="solid"), strip.text.y = element_text(color = "grey25", size=7, angle=0)) 
#p.region.year.bubbleplot.barplot.facet.lineage

# Make proportional hbar plot of HIV status
p.region.hiv.hbarplot <- ggplot(PHE.geo.HIV.counts, aes(count.per.region.hiv,phe_centre,fill=hivpos)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="HIV +ve",values=PHE.hiv.cols$hiv.cols, breaks=PHE.hiv.cols$hivpos) +
  labs(y="UKHSA Region", x="HIV +ve") +
  guides(fill=guide_legend(nrow=3)) +
  geom_text(data=PHE.geo.HIV.counts, aes(cum_fract.mid, phe_centre,label=count.per.region.hiv), size=theme.text.size.within, inherit.aes = F) +
  NULL
#p.region.hiv.hbarplot

p.region.orientation.hbarplot <- ggplot(PHE.geo.orientation.counts, aes(count.per.region.orientation,phe_centre,fill=gender_orientation)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="Orientation",values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) +
  labs(y="UKHSA Region", x="Orientation") +
  guides(fill=guide_legend(ncol=1)) +
  geom_text(data=PHE.geo.orientation.counts, aes(cum_fract.mid, phe_centre,label=count.per.region.orientation), size=theme.text.size.within, inherit.aes = F)
#p.region.orientation.hbarplot

p.region.ukborn.hbarplot <- ggplot(PHE.geo.UKborn, aes(Count,phe_centre,fill=ukborn)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="UK Born",values=PHE.ukborn.cols$ukborn.cols, breaks=PHE.ukborn.cols$ukborn) +
  labs(y="UKHSA Region", x="UK Born") +
  guides(fill=guide_legend(nrow=3)) +
  geom_text(data=PHE.geo.UKborn, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.region.ukborn.hbarplot

p.region.London.hbarplot <- ggplot(PHE.geo.London, aes(Count,phe_centre,fill=london)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="London",values=PHE.london.cols$london.cols, breaks=PHE.london.cols$london) +
  labs(y="UKHSA Region", x="London") +
  guides(fill=guide_legend(nrow=3)) +
  geom_text(data=PHE.geo.London, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.region.London.hbarplot

p.region.Age.hbarplot <- ggplot(PHE.geo.Age, aes(Count,phe_centre,fill=age_group)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="Age\nGroup",values=PHE.Age.cols$age_group.cols, breaks=PHE.Age.cols$age_group) +
  labs(y="UKHSA Region", x="Age Group") +
  guides(fill=guide_legend(ncol=1)) +
  geom_text(data=PHE.geo.Age, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.region.Age.hbarplot
```
\
Combined plot
```{r, fig.width=12, fig.height=10}
PHE.region.combiplot.1 <- plot_grid(p.region.year.bubbleplot, p.region.hbarplot + y.theme.strip, p.region.orientation.hbarplot + y.theme.strip, p.region.hiv.hbarplot + y.theme.strip, p.region.Age.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(4,2,2,2,2), scale=0.9)

PHE.region.combiplot.1
```


\
Regions as a complex multipanel plot
```{r, fig.width=10, fig.height=4.5}


# legends
PHE.region.combiplot.1.legends <- plot_grid(get_legend(p.region.year.bubbleplot), get_legend(p.region.hbarplot + y.theme.strip), get_legend(p.region.orientation.hbarplot + y.theme.strip), get_legend(p.region.hiv.hbarplot + y.theme.strip), get_legend(p.region.Age.hbarplot + y.theme.strip), nrow=1, align="h", rel_widths=c(6,4,4,4,4), scale=0.95)


# Arrange plots vertically
p.year.bubbleplot.combi <- plot_grid(p.all.year.bubbleplot + x.theme.strip, p.region.year.bubbleplot + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

p.region.hbar.counts.combi <- plot_grid(p.all.hbarplot + x.theme.strip + y.theme.strip, p.region.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

p.region.hbar.orientation.combi <- plot_grid(p.all.orientation.hbarplot + x.theme.strip + y.theme.strip, p.region.orientation.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

p.region.hbar.hiv.combi <- plot_grid(p.all.hiv.hbarplot + x.theme.strip + y.theme.strip, p.region.hiv.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

p.region.hbar.Age.combi <- plot_grid(p.all.Age.hbarplot + x.theme.strip + y.theme.strip, p.region.Age.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

# Combine the plots
p.region.hbar.combi.plus.all <- plot_grid(p.year.bubbleplot.combi, p.region.hbar.counts.combi, p.region.hbar.orientation.combi, p.region.hbar.hiv.combi, p.region.hbar.Age.combi, nrow=1, rel_widths=c(6,4,4,4,4), labels = c("A","B","C","D","E"), label_size=panel.lab.size, vjust=0.25)
# and add the legends on top
p.region.hbar.combi.plus.all.with.legends <- plot_grid(p.region.hbar.combi.plus.all, PHE.region.combiplot.1.legends, ncol=1, rel_heights=c(6,1), scale = 0.95)



p.region.hbar.combi.plus.all.with.legends
#ggsave(paste0(Figure_output_directory, "SupFig2_TPA-PHE_Sample-metadistros-by-phe_region+all-combi.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=240, height=135, device='pdf', dpi=1200)

```
\
\
Now lets look at some genetic data
\
### Make ML tree with sublineage tippoints
```{r}
TPA.MLtree.ggtree.tippoint <- TPA.MLtree.ggtree %<+% data.frame(Sample_Name=TPA.meta2.1$Sample_Name, Sublineage=TPA.meta2.1$TPA.pinecone.sublineage,stringsAsFactors = F) + 
  geom_tippoint(aes(color=Sublineage), size=0.5, alpha=0.5, show.legend = FALSE) + 
  scale_color_manual(name="Sublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage)
```
\
Add metadata
```{r, fig.width=10, fig.height=10}
# Continent
p.TPA.MLtree.PHE <- gheatmap(TPA.MLtree.ggtree.tippoint,
               TPA.rawseq.continents.p, color=NULL,width=0.075,offset=0.00000025, colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) + 
  scale_fill_manual(name="Continent",values=continental.cols.brew2$continent.col, breaks=continental.cols.brew2$Continent, guide = guide_legend(order = 1,ncol=2)) +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
  new_scale_fill()

# is UK
p.TPA.MLtree.PHE <- gheatmap(p.TPA.MLtree.PHE,
               TPA.rawseq.UK.p, color=NULL,width=0.075,offset=0.00001025, colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) + 
  scale_fill_manual(name="England/Other", values=c("black","grey95"), breaks=c("England","Other"), guide = guide_legend(order = 2,ncol=2)) +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
  new_scale_fill()

# Lineage
p.TPA.MLtree.PHE <- gheatmap(p.TPA.MLtree.PHE,TPA.rawseq.Lineage.p, color=NULL,width=0.075,offset=0.00002025, colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) + 
  scale_fill_manual(name="Lineage",values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage, guide = guide_legend(order = 3, ncol=2)) + theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
   new_scale_fill() +
  NULL

# sublineage
p.TPA.MLtree.PHE <- gheatmap(p.TPA.MLtree.PHE, data.frame(row.names=TPA.meta2.1$Sample_Name, Sublineage=TPA.meta2.1$TPA.pinecone.sublineage,stringsAsFactors = F), color=NULL,width=0.075,offset=0.00003025, colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) + 
  scale_fill_manual(name="Sublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage, guide = guide_legend(order = 4, ncol=3)) + theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
   new_scale_fill() +
  NULL
```
\
plot
```{r, fig.width=10, fig.height=10}
p.TPA.MLtree.PHE

#ggsave(paste0(Figure_output_directory, "SupFig3_TPA-PHE_Global_Phylo+UK-highlights.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=185, height=160, device='pdf', dpi=1200)
```
\
\
### Geographic distributions of Lineages and Sublineages
What about sublineages?
```{r}
p.region.Lineage.hbarplot <- ggplot(PHE.geo.Lineage, aes(Count,phe_centre,fill=TPA_Lineage)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="TPA\nLineage",values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage) +
  labs(y="UKHSA Region", x="TPA Lineage") +
  guides(fill=guide_legend(nrow=3)) +
  #geom_text(data=PHE.geo.Lineage, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F) +
  NULL

p.region.sublineage.hbarplot <- ggplot(PHE.geo.sublineage, aes(Count,phe_centre,fill=TPA.pinecone.sublineage)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="TPA\nSublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage) +
  labs(y="UKHSA Region", x="TPA Sublineage") +
  guides(fill=guide_legend(nrow=4)) +
  #geom_text(data=PHE.geo.sublineage, aes(cum_fract.mid, phe_centre,label=Count), size=theme.text.size.within, inherit.aes = F) +
  NULL

```
\
Combi plot (geography lineages)
```{r}
PHE.region.combiplot.2.lineages <- plot_grid(p.region.year.bubbleplot +legend.strip, p.region.hbarplot + y.theme.strip + legend.strip + coord_cartesian(xlim=c(0,150)), p.region.Lineage.hbarplot + y.theme.strip +legend.strip, p.region.sublineage.hbarplot + y.theme.strip +legend.strip, nrow=1, align="h", rel_widths=c(6,3,4,4), scale=0.99, labels=c("C","D","E","F"), label_size=panel.lab.size)

# separate out the plot for the legends
p.region.year.bubbleplot.legend <- get_legend(p.region.year.bubbleplot)
p.region.hbarplot.legend <- get_legend(p.region.hbarplot + y.theme.strip)
p.region.Lineage.hbarplot.legend <- get_legend(p.region.Lineage.hbarplot + y.theme.strip)
p.region.sublineage.hbarplot.legend <- get_legend(p.region.sublineage.hbarplot + y.theme.strip)

PHE.region.combiplot.2.lineages.legend <- plot_grid(p.region.year.bubbleplot.legend, p.region.hbarplot.legend, p.region.Lineage.hbarplot.legend, p.region.sublineage.hbarplot.legend, nrow=1, align="h", rel_widths=c(6,3,4,4))

PHE.region.combiplot.2.lineages <- plot_grid(PHE.region.combiplot.2.lineages, PHE.region.combiplot.2.lineages.legend, rel_heights = c(4,1), ncol=1)

PHE.region.combiplot.2.lineages
```
\
OK, let's now add a map of these geographical distributions

\
Let's used ONS published shape files - there is one available that shows Public Health England region boundaries. 
```{r}

# Generate approximate regional GPS coords
PHE.region.GPS <- data.frame(
  stringsAsFactors = FALSE,
          phe_centre = c("East Midlands",
                         "East of England","London","North East","North West",
                         "South East","South West","West Midlands",
                         "Yorkshire and Humber","UK (not England)","Not Known"),
            Longitude = c(-0.7,0.5,-0.2,-1.9,-2.4,
                         0.05,-2.9,-2,-0.8,0.1,0.63),
           Latitude = c(52.9,52.4,51.5,55,53.7,
                         51.1,51,52.6,53.8,54.7,54.1)
  )  
PHE.region.GPS <- left_join(PHE.region.GPS, PHE.geo.Lineage[PHE.geo.Lineage$TPA_Lineage=="SS14",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS)[4] <- "SS14"
PHE.region.GPS <- left_join(PHE.region.GPS, PHE.geo.Lineage[PHE.geo.Lineage$TPA_Lineage=="Nichols",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS)[5] <- "Nichols"
PHE.region.GPS[is.na(PHE.region.GPS)] <- 0

PHE.region.GPS <- left_join(PHE.region.GPS, PHE.geo.Lineage[PHE.geo.Lineage$TPA_Lineage=="SS14",c("phe_centre","total.region")], by="phe_centre")
colnames(PHE.region.GPS)[6] <- "Region_Count"

PHE.region.GPS$radius <- 0.5*(1-1/sqrt(PHE.region.GPS$Region_Count))


###############################
# Import datafile from https://geoportal.statistics.gov.uk/datasets/public-health-england-centres-december-2016-full-clipped-boundaries-in-england/explore?location=52.950000%2C-2.000000%2C6.88

UK.shapefile <- readOGR(dsn=UK.publichealth.shapefile.data)

#Reshape for ggplot2 using the Broom package
UK.mapdata <- tidy(UK.shapefile, region="phec16nm")

#UK.gg <- ggplot() + geom_polygon(data = UK.mapdata, aes(x = long, y = lat, group = group), color = "#FFFFFF", size = 0.25)
UK.gg <- ggplot() + geom_polygon(data = UK.mapdata, aes(x = long, y = lat, group = group), color="grey25", fill="grey90", size = 0.075)

#UK.gg <- UK.gg + coord_fixed(1) + theme_nothing()
#UK.gg
# Map plotting file becomes _very_ big - use ggrastr to reduce the size
UK.gg <-ggplot() + ggrastr::rasterise(geom_polygon(data = UK.mapdata, aes(x = long, y = lat, group = group), color="grey25", fill="grey90", size = 0.075), dpi=400) + coord_fixed(1) + theme_nothing()

#rasterise(geom_point(aes(carat, price, colour = cut), data=diamonds), dpi=30)



# Convert UK regions to be compatible with map
# First find centre point for each region
UK.mapdata.regions.meancoords <- UK.mapdata %>% dplyr::group_by(id) %>%
  dplyr::summarise(mean.lat=mean(lat), mean.long=median(long)) %>%
  dplyr::ungroup()
colnames(UK.mapdata.regions.meancoords)[1] <- "phe_centre"

PHE.region.GPS.ukmap <- dplyr::left_join(PHE.region.GPS, UK.mapdata.regions.meancoords, by="phe_centre")

# Add artificial location for 'not known'
PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre=="Not Known","mean.lat"] <- 600000
PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre=="Not Known","mean.long"] <- 550000

# Shift "South East" slightly to reduce the overlap with London
PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre=="South East","mean.long"] <- 475000
# Shift "East of England East" slightly to reduce the overlap with London 
PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre=="East of England","mean.lat"] <- 275000

# Not going to try plotting the 2 samples from elsewhere in the UK, so remove that row
PHE.region.GPS.ukmap <- PHE.region.GPS.ukmap[PHE.region.GPS.ukmap$phe_centre != "UK (not England)",]

# Create radius variable for plotting pie sizes (use log10(n)*20,000)
PHE.region.GPS.ukmap$radius.UK <- log10(PHE.region.GPS.ukmap$Region_Count)*20000

#PHE.geo.count.years.lineage

UK.gg.scatterpie <- UK.gg + geom_scatterpie(data=PHE.region.GPS.ukmap, aes(mean.long, mean.lat, group=phe_centre, r=radius.UK), alpha=0.85, color=NA, cols=c("Nichols","SS14")) + 
  scale_fill_manual(name="TPA\nLineage",values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage) + theme(legend.position="top")

UK.gg.scatterpie <- UK.gg.scatterpie + geom_scatterpie_legend(PHE.region.GPS.ukmap[!is.na(PHE.region.GPS.ukmap$mean.lat),"radius.UK"], labeller=function(x) round((10^(x/20000)),0), n=3, x=150000, y=500000)

UK.gg.scatterpie <- UK.gg.scatterpie + theme_nothing()

#? Add labels
UK.gg.scatterpie.labs <- UK.gg.scatterpie + geom_label_repel(data=PHE.region.GPS.ukmap[!is.na(PHE.region.GPS.ukmap$mean.lat),], aes(mean.long, mean.lat, label=phe_centre), size=theme.text.size.within, nudge_x = 50000, nudge_y = -25000, segment.size  = 0.1) + theme(legend.key.size = unit(0.55,"line"), legend.position="bottom") + 
  theme.text.size +
  theme_nothing()

UK.gg.scatterpie.labs
```
\
\
Now do an equivalent plot for sublineages
```{r}
PHE.region.GPS.ukmap.sublin <- PHE.region.GPS.ukmap


PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="1",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[11] <- "1"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="2",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[12] <- "2"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="3",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[13] <- "3"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="6",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[14] <- "6"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="8",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[15] <- "8"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="14",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[16] <- "14"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="15",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[17] <- "15"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="16",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[18] <- "16"
PHE.region.GPS.ukmap.sublin <- left_join(PHE.region.GPS.ukmap.sublin, PHE.geo.sublineage[PHE.geo.sublineage$TPA.pinecone.sublineage=="Singleton",c("phe_centre","Count")], by="phe_centre")
colnames(PHE.region.GPS.ukmap.sublin)[19] <- "Singleton"
PHE.region.GPS.ukmap.sublin[is.na(PHE.region.GPS.ukmap.sublin)] <- 0

# Most samples are either sublineage 1 or 14. Let's create a count of samples that are neither.
PHE.region.GPS.ukmap.sublin$`Other Sublineages` <- sapply(1:nrow(PHE.region.GPS.ukmap.sublin), function (x) PHE.region.GPS.ukmap.sublin$Region_Count[x]-sum(PHE.region.GPS.ukmap.sublin$`1`[x], PHE.region.GPS.ukmap.sublin$`14`[x])) 



UK.gg.scatterpie.sublineage <- UK.gg + geom_scatterpie(data=PHE.region.GPS.ukmap.sublin[PHE.region.GPS.ukmap.sublin$mean.long!=0,], aes(mean.long, mean.lat, group=phe_centre, r=radius.UK), alpha=0.85, color=NA, cols=c("1","14","Other Sublineages")) + 
  scale_fill_manual(name="TPA\nSublineage",values=c("#FC9272","#BCBDDC", "grey50"), breaks=c("1","14","Other Sublineages"))

# add legend
UK.gg.scatterpie.sublineage <- UK.gg.scatterpie.sublineage + geom_scatterpie_legend(PHE.region.GPS.ukmap[!is.na(PHE.region.GPS.ukmap$mean.lat),"radius.UK"], labeller=function(x) round((10^(x/20000)),0), n=3, x=150000, y=500000)

#UK.gg.scatterpie <- UK.gg.scatterpie + x.theme.strip + y.theme.strip
UK.gg.scatterpie.sublineage <- UK.gg.scatterpie.sublineage + theme_nothing()

#? Add labels
UK.gg.scatterpie.sublineage <- UK.gg.scatterpie.sublineage + geom_label_repel(data=PHE.region.GPS.ukmap[!is.na(PHE.region.GPS.ukmap$mean.lat),], aes(mean.long, mean.lat, label=phe_centre), size=theme.text.size.within, nudge_x = 50000, nudge_y = -25000, segment.size  = 0.1) +
  theme(legend.key.size = unit(0.55,"line"), legend.position="bottom") + 
  theme.text.size +
  theme_nothing()


UK.gg.scatterpie.sublineage
```

\
Combined map plot
```{r}
UK.gg.scatterpie.combi <- plot_grid(UK.gg.scatterpie.labs, UK.gg.scatterpie.sublineage, ncol=2, labels = c("A","B"), label_size=panel.lab.size)

UK.gg.scatterpie.combi
```
\
\
Plot in combination with barplots
```{r, fig.height=8, fig.width=8}
plot_grid(UK.gg.scatterpie.combi, PHE.region.combiplot.2.lineages, nrow=2, rel_heights=c(4,5))

#ggsave(paste0(Figure_output_directory,"Fig2_TPA-PHE_Map-Lineage+Barplots.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=190, height=185, device='pdf', dpi=1200)
```
\
\
### Analysis by sublineage
\
Now lets start exploring how samples are distributed by sublineage

```{r}
PHE.metadata.linked <- PHE.metadata.linked
PHE.metadata.linked$TPA.pinecone.sublineage <- factor(PHE.metadata.linked$TPA.pinecone.sublineage, levels=rev(as.character(sort(unique(PHE.metadata.linked$TPA.pinecone.sublineage)))))

PHE.Lineage.count <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA_Lineage) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total=sum(Count), perc=(Count/total)*100)

PHE.sublin.count <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total=sum(Count), perc=(Count/total)*100)

PHE.geo.sublin.years <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage,year) %>%
  dplyr::summarise(Count=n())


## Generate some stats about sublineage groups

# Generate some stats about gender orientation
PHE.sublineage.orientation.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage,gender_orientation) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.sublin=sum(Count)) %>%
  dplyr::arrange(desc(gender_orientation), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))


# Generate some stats about UK born
PHE.sublineage.UKborn <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage, ukborn) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.sublin=sum(Count)) %>%
  #dplyr::arrange(desc(ukborn), .by_group=T) %>%
  dplyr::arrange(desc(ukborn), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))
  
# Generate some stats about London based
PHE.sublineage.London <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage, london) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.sublin=sum(Count)) %>%
  dplyr::arrange(desc(london), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))

# Generate some stats about Age group
PHE.sublineage.Age <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage, age_group) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.sublin=sum(Count)) %>%
  dplyr::arrange(desc(age_group), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))

# Generate some stats about HIV group
PHE.sublineage.HIV <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage, hivpos) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.sublin=sum(Count)) %>%
  dplyr::arrange(desc(hivpos), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))

# Generate some stats by PHE Region
PHE.sublineage.PHEcentre <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage, phe_centre) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.sublin=sum(Count)) %>%
  dplyr::arrange(desc(phe_centre), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))

```

\
Plot by sublineage
```{r}
p.sublineage.year.bubbleplot <- ggplot(PHE.geo.sublin.years, aes(as.numeric(year), TPA.pinecone.sublineage, colour=TPA.pinecone.sublineage)) +
  geom_point(alpha=0.65, aes(size=Count)) + 
  geom_line(alpha=0.25) +
  guides(colour='none') +
  scale_size_area(max_size = 7,breaks=c(1,5,10,25,50)) + 
  guides(size=guide_legend(nrow=2, direction = 'horizontal', byrow=T)) +
  theme_light() +
  scale_color_manual(name="TPA\nSublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage) +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  labs(y="TPA Sublineage", x="Sample Year", size="Count") 
#p.sublineage.year.bubbleplot

p.sublineage.hbarplot <- ggplot(PHE.sublin.count, aes(Count,TPA.pinecone.sublineage,fill=TPA.pinecone.sublineage)) +
  geom_barh(stat="identity", position="stack", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="TPA\nSublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage) +
  labs(y="TPA Sublineage", x="Sample Count") +
  geom_text(data=PHE.sublin.count, aes((Count+12), TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F) +
  #coord_cartesian(xlim=c(0,200)) +
  coord_cartesian(xlim=c(0,260)) +
  guides(fill=guide_legend(ncol=2))
#p.sublineage.hbarplot 

p.sublineage.orientation.hbarplot <- ggplot(PHE.sublineage.orientation.counts, aes(y=TPA.pinecone.sublineage,x=Count,fill=gender_orientation)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="Orientation",values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) +
  labs(y="TPA Sublineage", x="Orientation") +
  guides(fill=guide_legend(ncol=1)) +
  geom_text(data=PHE.sublineage.orientation.counts, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.region.orientation.hbarplot

p.sublineage.hiv.hbarplot <- ggplot(PHE.sublineage.HIV, aes(y=TPA.pinecone.sublineage, x=Count,fill=hivpos)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="HIV +ve",values=PHE.hiv.cols$hiv.cols, breaks=PHE.hiv.cols$hivpos) +
  labs(y="TPA Sublineage", x="HIV +ve") +
  guides(fill=guide_legend(ncol=1)) + 
  geom_text(data=PHE.sublineage.HIV, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.sublineage.hiv.hbarplot

p.sublineage.ukborn.hbarplot <- ggplot(PHE.sublineage.UKborn, aes(y=TPA.pinecone.sublineage,x=Count,fill=ukborn)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="UK\nborn",values=PHE.ukborn.cols$ukborn.cols, breaks=PHE.ukborn.cols$ukborn) +
  labs(y="TPA Sublineage", x="UK born") +
  guides(fill=guide_legend(nrow=3)) +
  geom_text(data=PHE.sublineage.UKborn, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.sublineage.ukborn.hbarplot

p.sublineage.Age.hbarplot <- ggplot(PHE.sublineage.Age, aes(y=TPA.pinecone.sublineage, x=Count ,fill=age_group)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="Age\nGroup",values=PHE.Age.cols$age_group.cols, breaks=PHE.Age.cols$age_group) +
  labs(y="TPA Sublineage", x="Age Group") +
  guides(fill=guide_legend(ncol=1)) +
  geom_text(data=PHE.sublineage.Age, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)
#p.sublineage.Age.hbarplot


p.sublineage.PHEregion.hbarplot <- ggplot(PHE.sublineage.PHEcentre, aes(y=TPA.pinecone.sublineage, x=Count, fill=phe_centre)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  scale_fill_manual(name="UKHSA\nRegion",values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$PHE.region) +
  labs(y="TPA Sublineage", x="UKHSA Region") +
  guides(fill=guide_legend(nrow=4)) +
  geom_text(data=PHE.sublineage.PHEcentre, aes(cum_fract.mid, TPA.pinecone.sublineage,label=Count), size=theme.text.size.within, inherit.aes = F)

```
\
Look at how sublineages are distributed by region (sublineage-centric)
```{r}
p.sublineage.PHEregion.hbarplot
```

\
Combine patient metadata into a plot
```{r, fig.width=12, fig.height=10}
#PHE.sublineages.combiplot.1 <- plot_grid(p.sublineage.year.bubbleplot, p.sublineage.hbarplot + y.theme.strip, p.sublineage.orientation.hbarplot + y.theme.strip, p.sublineage.hiv.hbarplot + y.theme.strip, p.sublineage.PHEregion.hbarplot + y.theme.strip, p.sublineage.ukborn.hbarplot + y.theme.strip, p.sublineage.Age.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(3,2,2,2,2,2,2), scale=0.9)

#PHE.sublineages.combiplot.1 <- plot_grid(p.sublineage.year.bubbleplot, p.sublineage.hbarplot + y.theme.strip, p.sublineage.orientation.hbarplot + y.theme.strip, p.sublineage.hiv.hbarplot + y.theme.strip, p.sublineage.Age.hbarplot + y.theme.strip, p.sublineage.PHEregion.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(3,2,2,2,2,4), scale=0.9)

PHE.sublineages.combiplot.1 <- plot_grid(p.sublineage.year.bubbleplot, p.sublineage.hbarplot + y.theme.strip, p.sublineage.orientation.hbarplot + y.theme.strip, p.sublineage.hiv.hbarplot + y.theme.strip, p.sublineage.Age.hbarplot + y.theme.strip, nrow=1, align="h", rel_widths=c(4,2,2,2,2), scale=0.9)

PHE.sublineages.combiplot.1 

```


\
Lets add the 'all' row again to the 'by sublineage' plot
```{r, fig.height=5, fig.width=12}
# legends
PHE.sublineage.combiplot.1.legends <- plot_grid(get_legend(p.sublineage.year.bubbleplot), get_legend(p.sublineage.hbarplot + y.theme.strip), get_legend(p.sublineage.orientation.hbarplot + y.theme.strip), get_legend(p.sublineage.hiv.hbarplot + y.theme.strip), get_legend(p.sublineage.Age.hbarplot + y.theme.strip), nrow=1, align="h", rel_widths=c(6,4,4,4,4), scale=0.95)

# regions
#PHE.sublineage.combiplot.1.nolegend <- plot_grid(p.sublineage.year.bubbleplot + legend.strip, p.sublineage.hbarplot + y.theme.strip + legend.strip, p.sublineage.orientation.hbarplot + y.theme.strip + legend.strip, p.sublineage.hiv.hbarplot + y.theme.strip + legend.strip, p.sublineage.Age.hbarplot + y.theme.strip + legend.strip, nrow=1, align="h", rel_widths=c(4,2,2,2,2), scale=0.9)

# Or do it vertically
p.sublineage.year.bubbleplot.combi <- plot_grid(p.all.year.bubbleplot + x.theme.strip, p.sublineage.year.bubbleplot + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

p.sublineage.hbar.counts.combi <- plot_grid(p.all.hbarplot + x.theme.strip + y.theme.strip, p.sublineage.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

p.sublineage.hbar.orientation.combi <- plot_grid(p.all.orientation.hbarplot + x.theme.strip + y.theme.strip, p.sublineage.orientation.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

p.sublineage.hbar.hiv.combi <- plot_grid(p.all.hiv.hbarplot + x.theme.strip + y.theme.strip, p.sublineage.hiv.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

p.sublineage.hbar.Age.combi <- plot_grid(p.all.Age.hbarplot + x.theme.strip + y.theme.strip, p.sublineage.Age.hbarplot + y.theme.strip + legend.strip, ncol=1, align="v",axis="lr", rel_heights=c(1,7))

# Combine the plots
p.sublineage.hbar.combi.plus.all <- plot_grid(p.sublineage.year.bubbleplot.combi, p.sublineage.hbar.counts.combi, p.sublineage.hbar.orientation.combi, p.sublineage.hbar.hiv.combi, p.sublineage.hbar.Age.combi, nrow=1, rel_widths=c(7,3,4,4,4), labels=c("A", "B", "C", "D", "E"),label_size=panel.lab.size, vjust=1, scale=0.99)

# and add the legends on top
#p.sublineage.hbar.combi.plus.all.with.legends <- plot_grid(PHE.sublineage.combiplot.1.legends, p.sublineage.hbar.combi.plus.all, ncol=1, rel_heights=c(1,9))

# legends below
p.sublineage.hbar.combi.plus.all.with.legends <- plot_grid(p.sublineage.hbar.combi.plus.all, PHE.sublineage.combiplot.1.legends, ncol=1, rel_heights=c(8,1))


p.sublineage.hbar.combi.plus.all.with.legends

```

\
\ 
These patterns look fairly similar between sublineages, and (apart from 1 & 14) the groups are very small. However, sublineage 14 does appear to have a higher proportion of MSM compared to sublineage 1 and others. Let's test that formally using 2x2 fisher's tests
\
```{r}
PHE.MSM.counts.all <- PHE.metadata.linked %>% 
  dplyr::group_by(is.MSM, .drop=F) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.sublin=sum(Count)) %>%
  dplyr::arrange((is.MSM), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))

PHE.sublineage.MSM.counts <- PHE.metadata.linked %>% 
  dplyr::group_by(TPA.pinecone.sublineage,is.MSM, .drop=F) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.sublin=sum(Count)) %>%
  dplyr::arrange((is.MSM), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2)) #%>%
  #dplyr::filter(!is.na(is.MSM))


PHE.sublineage.MSM.counts.wider <- PHE.sublineage.MSM.counts %>% dplyr::select(TPA.pinecone.sublineage, is.MSM, Count) %>%
  tidyr::pivot_wider(names_from = is.MSM, values_from=Count) %>%
  dplyr::mutate(MSM=replace_na(MSM, 0), Other=replace_na(Other, 0), Total=sum(MSM,Other)) %>%
  #dplyr::select(-`NA`) %>%
  dplyr::filter(Total!=0)
  

PHE.sublineage.MSM.pval <- data.frame(TPA.pinecone.sublineage=PHE.sublineage.MSM.counts.wider$TPA.pinecone.sublineage, p.fisher=sapply(1:nrow(PHE.sublineage.MSM.counts.wider), function (x) fisher.test(matrix(as.numeric(c(PHE.sublineage.MSM.counts.wider[x,"MSM"],
                                PHE.sublineage.MSM.counts.wider[x,"Other"],
                                PHE.MSM.counts.all[PHE.MSM.counts.all$is.MSM=="MSM","Count"], PHE.MSM.counts.all[PHE.MSM.counts.all$is.MSM=="Other","Count"])),nrow=2))[[1]]), stringsAsFactors=F)

PHE.sublineage.MSM.counts.wider <- dplyr::left_join(PHE.sublineage.MSM.counts.wider, PHE.sublineage.MSM.pval, by="TPA.pinecone.sublineage")

PHE.sublineage.MSM.counts.wider
```


\
\
### Visualisation of UK genomic relationships
\
Ok, let's make a tree for displaying these relationships using the UK dataset only
\
From some experimentation, a 'GrapeTree' minimum spanning network works well for visualising the clonality of these populations. We can use a SNP-scaled phylogeny as direct input to GrapeTree, and this will allow branches to be scaled appropriately. However, although annotation is allowed within the GrapeTree software, colours must be manually edited. Final GrapeTree plots can then be imported back into R for combining with other plots. 
\

Alternative visualisations - grapetree?
\
Take the 526-global phylogeny (snp-scaled version from pyjar), and prune to only include the UK strains from this study - this ensures the topology is consistent accross studies. 
```{r}

TPA.pyjar.tree.subset.uk <- ape::keep.tip(TPA.pyjar.tree, as.character(unlist(PHE.metadata.linked[PHE.metadata.linked$Geo_Country=="UK","Sample_Name"])))


TPA.pyjar.tree.subset.global_beast_only.seqlanes <- TPA.meta2.1 %>% filter(full.temporal.analysis=='Yes') %>%
  select(Cleaned_fastq_id) %>% pull()

TPA.pyjar.tree.subset.uk.seqlanes <- as.character(unlist(PHE.metadata.linked[PHE.metadata.linked$Geo_Country=="UK","Cleaned_fastq_id"]))


ggtree(TPA.pyjar.tree.subset.uk)
#write.tree(TPA.pyjar.tree.subset.uk, paste0(Data_input_directory,"TPA.UK-only.pyjar.2022-02-03.tre"))

# Write out a metadata sheet for the relevant information
PHE.metadata.linked.grapetree <- PHE.metadata.linked[,c("Sample_Name", "year","gender_orientation","phe_centre","hivpos","ukborn","TPA_Lineage","TPA.pinecone.sublineage")]
colnames(PHE.metadata.linked.grapetree)[1] <- "ID"

#write.table(PHE.metadata.linked.grapetree, paste0(Data_input_directory,"TPA.UK-only.grapetree.meta.2022-02-03.tsv"), sep = "\t", quote=F, row.names = F)
```
\
Tree independently visualised and annotated using GrapeTree.
\
Now import and integrate GrapeTree plot with metadata plots.
```{r, fig.height=8, fig.width=10}
# Combine the plots
p.sublineage.hbar.combi.plus.all.B2F <- plot_grid(p.sublineage.year.bubbleplot.combi, p.sublineage.hbar.counts.combi, p.sublineage.hbar.orientation.combi, p.sublineage.hbar.hiv.combi, p.sublineage.hbar.Age.combi, nrow=1, rel_widths=c(7,4,4,4,4), labels=c("B", "C", "D", "E", "F"),label_size=panel.lab.size, vjust=1, scale=0.97)

# legends below
p.sublineage.hbar.combi.plus.all.with.legends.B2F <- plot_grid(p.sublineage.hbar.combi.plus.all.B2F, PHE.sublineage.combiplot.1.legends, ncol=1, rel_heights=c(7,1))

#p.sublineage.hbar.combi.plus.all.with.legends.B2F


# Now bring in externally plotted Grapetree
p.TPA.UK.Grapetree.sublineages <- ggdraw() + draw_image(TPA.UK.Grapetree.sublineages.file)
p.TPA.UK.Grapetree.sublineages

p.sublineage.hbar.combi.plus.all.with.legends.B2F.with.grapetree <- plot_grid(p.TPA.UK.Grapetree.sublineages, p.sublineage.hbar.combi.plus.all.with.legends.B2F, ncol=1, labels=c("A",""), label_size=panel.lab.size, rel_heights=c(3,5)) 


p.sublineage.hbar.combi.plus.all.with.legends.B2F.with.grapetree
#ggsave(paste0(Figure_output_directory, "Fig1_TPA-PHE_Sample-distros-sublineage.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=190, height=185, device='pdf', dpi=1200)
```

\
Manage other GrapeTree plots (for consistency)

TPA-UK-2022-02-16.-MSTree_3-way-figure.Inscaped-2
```{r}
# Bring in 3-way graphetree plot (3 different metadata variables using the same input tree)
TPA.UK.Grapetree.3way <- ggdraw() + draw_image(TPA.UK.Grapetree.3way.file)
TPA.UK.Grapetree.3way

#ggsave(paste0(Figure_output_directory, "SupFig4_TPA-PHE_Grapetree-3ways.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=145, height=180, device='pdf', dpi=1200)

```

\
And also do the HIV status plot
```{r}

TPA.UK.Grapetree.HIV <- ggdraw() + draw_image(TPA.UK.Grapetree.HIV.file)
TPA.UK.Grapetree.HIV

#ggsave(paste0(Figure_output_directory, "SupFig5_TPA-PHE_Grapetree-HIV.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=185, height=110, device='pdf', dpi=1200)

```



\
\
### Phylogenetic context analyses
\
Ok, now lets look at some trees
\
First, let's formalise BEAST tree plotting as three separate functions to enable other trees to be plotted the same way
\
```{r}
full.beast2.tree <- read.beast(full.beast2.tree.file)
full.beast2.tree@phylo$tip.label <- gsub("\\|.+$","",full.beast2.tree@phylo$tip.label, perl=T)

################################################################################################
# function to extract a tree based on sublineage
Extract_sublineage_tree_for_plot <- function(my.beast.tree, my.metadata, my.phe.meta, my.sublineage){
  # get all tips to include from metadata, then calculate MRCA from tree
  sublineage.test.mrca <- getMRCA(my.beast.tree@phylo, as.character(unlist(my.metadata[my.metadata$TPA.pinecone.sublineage==my.sublineage,"Sample_Name"])))
  ######
  TPA.beast.subtree.test <- tree_subset(my.beast.tree, node=sublineage.test.mrca, levels_back=0)
  return(TPA.beast.subtree.test)
}
#Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 1)
################################################################################################


################################################################################################
# Function to prepare a beast tree with timescale indicators, posterior support and 95% HPD bars
plot_beast_subtree_with_HPD <- function(my.beast.tree, my.metadata, my.phe.meta, mrsd.fulltree){
  # get MRCD for tree
  mrsd.Beast.tree.test.s <- max(as.numeric(unlist(my.metadata[my.metadata$Sample_Name %in% my.beast.tree@phylo$tip.label,"Sample_Year"])))
  mrsd.Beast.tree.test <- lubridate::ymd(paste0(mrsd.Beast.tree.test.s,"-06-01")) 
  mrsd.Beast.tree.fulltree <- lubridate::ymd(mrsd.fulltree) 
  #mrsd.Beast.tree.test
  # plot basic tree
  options(ignore.negative.edge=TRUE)
  p.TPA.beast.subtree.test <- ggtree(my.beast.tree, mrsd=mrsd.Beast.tree.test, ladderize = T, size=0.4) + scale_x_continuous(breaks=seq(1960,2020,10), minor_breaks=seq(2000, 2020, 1)) +
    theme_tree2() +
    # Add date lines for easy interpretation  
    theme(panel.grid.major   = element_line(color="grey50", size=.2),
          panel.grid.minor   = element_line(color="grey85", size=.2),
          panel.grid.major.y = element_blank(),
          panel.grid.minor.y = element_blank())
  # Add posterior support as node points
  p.TPA.beast.subtree.test <- p.TPA.beast.subtree.test + geom_point2(aes(subset=(!isTip & as.numeric(posterior)>0.8)),color="gray60",size=2,alpha=0.5, shape=18) + 
    geom_point2(aes(subset=(!isTip & as.numeric(posterior)>0.91)),color="gray40",size=3,shape=18,alpha=0.5) + 
    geom_point2(aes(subset=(!isTip & as.numeric(posterior)>=0.96)),color="black",size=3,shape=18,alpha=0.5)
  ######
  # extract 95% HPD intervals - geom_range seems unable to do correctly with this tree (known bug for tip dated trees), so extract data and plot using geom_segment
  TPA.beast.subtree.test.data <- fortify(my.beast.tree)
  minmax <- t(matrix(unlist(TPA.beast.subtree.test.data[!is.na(TPA.beast.subtree.test.data$height_0.95_HPD),"height_0.95_HPD"]),nrow=2))
  bar_df <- data.frame(node_id=TPA.beast.subtree.test.data[!is.na(TPA.beast.subtree.test.data$height_0.95_HPD),"node"],as.data.frame(minmax))
  names(bar_df) <- c('node_id','min','max') 
  bar_df <- bar_df %>% filter(node_id > Ntip(my.beast.tree@phylo))
  bar_df <- bar_df %>% left_join(TPA.beast.subtree.test.data, by=c('node_id'='node')) #%>% select(node_id,min,max,y)
  #mrcd.decimal <- decimal_date(mrsd.Beast.tree.test)
  mrcd.decimal <- decimal_date(mrsd.Beast.tree.fulltree)
  
  # Now add HPDs to plot
  p.TPA.beast.subtree.test <- p.TPA.beast.subtree.test + geom_segment(aes(x=mrcd.decimal-max, y=y, xend=mrcd.decimal-min, yend=y), data=bar_df, color='red', alpha=0.2, size=2.0)
  # Output tree 
  return(p.TPA.beast.subtree.test)
}
################################################################################################


################################################################################################
# Function to add metadata to tree
# Has two optional arguments "initial.track.offset" and "track.scaling" which can be used to alter the width and positioning of metadata tracks

plot_beast_subtree_with_PHE_metadata <- function(my.beast.tree.input, my.metadata, my.phe.meta, initial.track.offset, track.scaling){
    # Add code to allow scaling up of the track offsets and widths - useful for much bigger length trees
  if(missing(initial.track.offset)){
    initial.track.offset <- 0
  }    
  if(missing(track.scaling)){
    track.scaling <- 1
  }
  # Calculate amount to offset each heatmap track
  offset.dist <- 4*track.scaling
  track.width <- (1/max(my.beast.tree.input$data$height)*3)*track.scaling
  
  # make a list of taxa used in this plot 
  my.taxa.list <- as.character(unlist(filter(my.beast.tree.input$data, isTip==TRUE) %>% select(label)))
  
  # make a color scale for sampling years
  #PHE.sublintest.year.cols <- data.frame(year=sort(unique(as.numeric(unlist(my.metadata[(my.metadata$Sample_Name %in% my.taxa.list),"Sample_Year"],use.names=F)))),stringsAsFactors = T)
  #PHE.sublintest.year.cols$year.cols <- colorRampPalette(brewer.pal(7, "YlOrRd"))(nrow(PHE.sublintest.year.cols))
  
  # Or alternatively, use a common colour scheme for all data (maybe more sensible)
  PHE.sublintest.year.cols <- data.frame(year=TPA.year.cuttoff.cols$date.cuttoff, year.cols=TPA.year.cuttoff.cols$date.cuttoff.col, stringsAsFactors = F)
  
  # make metadata file for UK regions present in sublineage
  sublin.test.region.meta <- data.frame(row.names=as.character(unlist(my.phe.meta[my.phe.meta$Sample_Name %in% my.taxa.list,"Sample_Name"])), Region=as.character(unlist(my.phe.meta[my.phe.meta$Sample_Name %in% my.taxa.list,"phe_centre"])), stringsAsFactors = F)
  
  # Add heatmap strips
  # Sample Year
  #TPA.beast.subtree.test.global.plot1.regional <- gheatmap(my.beast.tree.input, TPA.rawseq.all.Years.p, color=NULL,width=track.width, offset=initial.track.offset+offset.dist,colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
    #scale_fill_manual(name="Year", values=PHE.sublintest.year.cols$year.cols,breaks=PHE.sublintest.year.cols$year, guide = guide_legend(order = 1, ncol=2)) +
    #ggnewscale::new_scale_fill()
  TPA.beast.subtree.test.global.plot1.regional <- gheatmap(my.beast.tree.input, TPA.rawseq.year.cuttoff.p, color=NULL,width=track.width, offset=initial.track.offset+offset.dist,colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) +
    scale_fill_manual(name="Year", values=PHE.sublintest.year.cols$year.cols,breaks=PHE.sublintest.year.cols$year, guide = guide_legend(order = 1, ncol=2)) +
    ggnewscale::new_scale_fill()
  
  # Add country
  TPA.beast.subtree.test.global.plot1.regional <- gheatmap(TPA.beast.subtree.test.global.plot1.regional, TPA.rawseq.countries.p, color=NULL,width=track.width, offset=initial.track.offset+(offset.dist*2),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) + 
    scale_fill_manual(name="Country", values=continental.country.cols.brew2$country.col, breaks=continental.country.cols.brew2$Geo_Country, guide = guide_legend(order = 2)) +
    ggnewscale::new_scale_fill()
  # UK or non-UK
  TPA.beast.subtree.test.global.plot1.regional <- gheatmap(TPA.beast.subtree.test.global.plot1.regional,
                                                           TPA.rawseq.UK.p, color=NULL,width=track.width,offset=initial.track.offset+(offset.dist*3), colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=theme.text.size.within) + 
    scale_fill_manual(name="England/Other", breaks=c("England","Other"), values=c("black","grey95"), na.value = "white", guide = guide_legend(order = 3, ncol=2)) +
    ggnewscale::new_scale_fill()
  # UK PHE region
  TPA.beast.subtree.test.global.plot1.regional <- gheatmap(TPA.beast.subtree.test.global.plot1.regional, sublin.test.region.meta, color=NULL,width=track.width, offset=initial.track.offset+(offset.dist*4),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) + 
    scale_fill_manual(name="UKHSA Region", values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region, na.value = "white", guide = guide_legend(order = 4)) +
    ggnewscale::new_scale_fill()
  
  # TPA sublineage
  #TPA.beast.subtree.test.global.plot1.regional <- gheatmap(TPA.beast.subtree.test.global.plot1.regional, data.frame(row.names=TPA.meta2.1$Sample_Name, Sublineage=TPA.meta2.1$TPA.pinecone.sublineage, stringsAsFactors = F), color=NULL,width=track.width,offset=initial.track.offset+(offset.dist*5), colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0,font.size=2.5) + 
  #scale_fill_manual(name="Sublineage",values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage, guide = guide_legend(order = 5)) 
  
  TPA.beast.subtree.test.global.plot1.regional <- TPA.beast.subtree.test.global.plot1.regional + theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
    new_scale_fill() +
    geom_rootedge(2) +
    NULL
  
  # calculate number of taxa
  test.taxacount <- length(my.taxa.list)
  # Adjust final plot x and y axis to make space for labels using taxa counts
  x.axis.limits <- ggplot_build(TPA.beast.subtree.test.global.plot1.regional)$layout$panel_scales_x[[1]]$range$range
  TPA.beast.subtree.test.global.plot1.regional <- TPA.beast.subtree.test.global.plot1.regional + 
    coord_cartesian(y=c(-0.5-(test.taxacount/15),test.taxacount+2), x=c(x.axis.limits[1],x.axis.limits[2]+3))
  
  return(TPA.beast.subtree.test.global.plot1.regional)
}
################################################################################################

```

\
Great, now let's plot a full beast tree
```{r, fig.height=10, fig.width=10}
# function for x-axis time breaks needs tweaking for the full tree
TPA.Global.full.BeastTree.ukmeta <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(my.beast.tree = full.beast2.tree, my.metadata = TPA.meta2.1, my.phe.meta = PHE.metadata.linked, mrsd.fulltree = "2019-06-01") + scale_x_continuous(breaks=seq(1400,2020,50), minor_breaks=seq(1950, 2020, 5)), my.metadata = TPA.meta2.1, my.phe.meta = PHE.metadata.linked, track.scaling = 5)

TPA.Global.full.BeastTree.ukmeta

#ggsave(paste0(Figure_output_directory,"SupFig7_TPA_FullBeastTree.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=185, height=240, device='pdf', dpi=1200)
```

\
Now do sublineage plots
\
Make some plots
```{r, warning=FALSE}
# Sublineage 1
sublineage.1.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 1), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, track.scaling = 1.2)

# Sublineage.2
sublineage.2.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 2), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, track.scaling = 1)

# Sublineage.8
sublineage.8.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 8), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, track.scaling = 1.1)

# Sublineage.14
sublineage.14.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 14), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, track.scaling = 1.1)

```

\
Plot together?
\
Maybe with sublineage 1 expanded?
```{r, fig.width=12, fig.height=12}
p.beast.trees.heatmap.sublineages.combi.offset1 <- plot_grid(sublineage.2.tree.heatmap, 
          sublineage.8.tree.heatmap, 
          sublineage.14.tree.heatmap, 
          ncol=2, labels=c("B - Sublineage 2","C - Sublineage 8","D - Sublineage 14"), label_size=panel.lab.size, scale=0.95, vjust=1.0)

p.beast.trees.heatmap.sublineages.combi.offset2 <- plot_grid(sublineage.1.tree.heatmap, p.beast.trees.heatmap.sublineages.combi.offset1, labels=c("A - Sublineage 1", ""), label_size=panel.lab.size, scale=0.975, ncol=2, rel_widths=c(6,11), vjust=2.5)


p.beast.trees.heatmap.sublineages.combi.offset2
#ggsave(paste0(Figure_output_directory,"SupFig8_TPA-PHE_Sublineage-BeastTrees.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=265, height=230, device='pdf', dpi=1200)
```

\
Need to explore sublineage 14 a bit more to get dates for those subclades
```{r}
sublineage.14.tree.heatmap + geom_tiplab(size=theme.text.size.within, linesize=0.4) #3
```

\
```{r}
# Ok, there are multiple subclades in this tree
sublineage.14.tree.heatmap.data <- sublineage.14.tree.heatmap$data

# getMRCA(full.beast2.tree@phylo,c("PHE150150A","NL14","TPA_BCC122","TPA_BCC126","PHE140076A","TPA_UKBRG008"))  982
# full.beast2.tree@phylo$tip.label[phangorn::Descendants(full.beast2.tree@phylo, 982, type = c("tips"))[[1]]]

sublineage.14.lowerclade.list <- c("NL17", "NL19", "PHE140085A", "PHE140089A", "PHE150118A", "PHE150121A", "PHE150133A", "PHE150143A", "PHE150145A", "PHE150162A", "PHE150166A", "PHE150168A", "PHE160224A", "PHE160243A", "PHE160255A", "PHE160276A", "PHE160290A", "PHE160302A", "PHE160306A", "PHE170333A", "PHE170349A", "PHE170374A", "PHE170381A", "PHE170664A", "TPA_ESBCN005", "TPA_UKBIR032")

sublineage.14.upperclade.list <- c("NL14", "PHE140076A", "PHE150149A", "PHE150150A", "PHE150170A", "PHE160196A", "PHE160263A", "PHE160274A", "PHE160287A", "PHE160294A", "PHE160316A", "PHE160317A", "PHE170372A", "PHE170386A", "PHE170397A", "PHE170405A", "TPA_BCC081", "TPA_BCC088", "TPA_BCC089", "TPA_BCC101", "TPA_BCC122", "TPA_BCC126", "TPA_BCC136", "TPA_BCC169", "TPA_HUN180004", "TPA_HUN190020", "TPA_UKBIR044", "TPA_UKBRG007", "TPA_UKBRG008")

# Get MRCA date for lower clade
sublineage.14.lowerclade.list.tmrca <- sublineage.14.tree.heatmap.data[sublineage.14.tree.heatmap.data$node==getMRCA(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 14)@phylo, sublineage.14.lowerclade.list),"x"]

paste0("TMRCA for sublineage 14 lower clade: ",sublineage.14.lowerclade.list.tmrca)

# Get MRCA date for upper clade
sublineage.14.upperclade.list.tmrca <- sublineage.14.tree.heatmap.data[sublineage.14.tree.heatmap.data$node==getMRCA(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 14)@phylo, sublineage.14.upperclade.list),"x"]

paste0("TMRCA for sublineage 14 upper clade: ",sublineage.14.upperclade.list.tmrca)
```
\
\
Extract key information for sublineage 6 (two samples)
```{r}
sublineage.6.tree.heatmap <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 6), TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked)

sublineage.6.tree.heatmap.data <- sublineage.6.tree.heatmap$data

# Get MRCA date for upper clade
sublineage.6.beasttree.tmrca <- as.numeric(sublineage.6.tree.heatmap.data[sublineage.6.tree.heatmap.data$node==getMRCA(Extract_sublineage_tree_for_plot(full.beast2.tree, TPA.meta2.1, PHE.metadata.linked, my.sublineage = 6)@phylo, c("PHE130048A", "PHE160283A")),"branch"])


paste0("TMRCA for sublineage 6 upper clade: ",sublineage.6.beasttree.tmrca)
```



\
\
### Extract sample & population statistics from datasets for use in manuscript text
\
Dataset and Geographical distributions
```{r}
# dataset counts
paste0("Total UK samples in cleaned/deduplicated dataset: ",nrow(PHE.metadata.linked))
paste0("Of which: ",nrow(PHE.metadata.linked[PHE.metadata.linked$is.PHE=="PHE",])," from PHE Ref lab at Colindale")
paste0("Of which: ",nrow(PHE.metadata.linked[PHE.metadata.linked$is.PHE=="Other",])," from other labs")

# proportion with geographical data
paste0("From UK samples, ", nrow(PHE.metadata.linked[(PHE.metadata.linked$phe_centre %notin% c("Not Known","UK (not England)")),])," were grouped into one of the 9 PH regions")
paste0("From UK samples, ", nrow(PHE.metadata.linked[PHE.metadata.linked$phe_centre=="UK (not England)",]), " were referred from outside England")
paste0("From UK samples, ", nrow(PHE.metadata.linked[PHE.metadata.linked$phe_centre=="Not Known",]), " had unknown region")

# counts & fractions by PHE region
PHE.geo.count

```
\
Gender Orientation stats
```{r}
PHE.orientation.counts
PHE.geo.orientation.counts
PHE.geo.HIV.counts
PHE.sublineage.orientation.counts
PHE.sublineage.Age
```

\
Sublineage Distributions
```{r}
PHE.Lineage.count
PHE.sublin.count
PHE.geo.sublineage
```

\
Macrolide resistance stats
```{r}
UK.macrolide.res <- PHE.metadata.linked %>%
  dplyr::group_by(A2058G, A2059G) %>%
  dplyr::summarise(Count.allele=n()) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(total.count=sum(Count.allele), perc.allele=round((Count.allele/total.count)*100,1))
UK.macrolide.res

UK.macrolide.res.sublin <- PHE.metadata.linked %>%
  dplyr::group_by(TPA.pinecone.sublineage, A2058G, A2059G) %>%
  dplyr::summarise(Count.allele=n()) %>%
  dplyr::ungroup() %>%
  dplyr::group_by(TPA.pinecone.sublineage) %>%
  dplyr::mutate(total.count=sum(Count.allele), perc.allele=round((Count.allele/total.count)*100,1))
UK.macrolide.res.sublin


# Calculate long form df, with different 23S alleles (A2058G, A2059G, WT, Uncertain) v.s. sublineage
UK.macrolide.res.sublin.long <- PHE.metadata.linked %>%
  mutate(Resistance.allele=ifelse(A2058G=="Yes", "A2058G", ifelse(A2059G=="Yes", "A2059G", ifelse((A2058G=="No" & A2059G=="No"),"Wild Type", "Uncertain")))) %>%
  dplyr::group_by(TPA.pinecone.sublineage, Resistance.allele) %>%
  dplyr::summarise(Count.per.sublin.Macrolides=n()) %>%
  dplyr::mutate(total.sublin=sum(Count.per.sublin.Macrolides), 
                fraction=Count.per.sublin.Macrolides/total.sublin) %>%
  #dplyr::ungroup() %>%
  dplyr::arrange((Resistance.allele), .by_group=T) %>%
  dplyr::mutate(cum_fract = cumsum(fraction)) %>%
  dplyr::mutate(cum_fract.mid = cum_fract-(fraction/2)) %>%
  dplyr::mutate(Resistance.allele = factor(Resistance.allele, levels=rev(c("A2058G", "A2059G", "Uncertain", "Wild Type"))))

# Make plot of macrolide resistance by sublineages
p.sublin.Macrolides.hbarplot <- ggplot(UK.macrolide.res.sublin.long, aes(Count.per.sublin.Macrolides, y=TPA.pinecone.sublineage, fill=Resistance.allele)) +
  geom_barh(stat="identity", position="fill", width=0.65) +
  theme_light() +
  scale_fill_manual(name="Macrolide\nResistance\nAllele",values=c("indianred2", "steelblue1","grey55", "grey90"), breaks=c("A2058G", "A2059G", "Uncertain", "Wild Type")) +
  labs(y="TPA Sublineage", x="Proportion with Macrolide Resistance Allele") +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='bottom') +
  guides(fill=guide_legend(ncol=2)) +
  geom_text(data=UK.macrolide.res.sublin.long, aes(cum_fract.mid, y=TPA.pinecone.sublineage,label=Count.per.sublin.Macrolides), size=theme.text.size.within, inherit.aes = F) +
  NULL

p.sublin.Macrolides.hbarplot


# Combine plot with sublineage count bars
p.sublin.Macrolides.hbarplot.combi <- plot_grid(p.sublineage.hbarplot + guides(fill=guide_legend(ncol=3)), p.sublin.Macrolides.hbarplot + y.theme.strip, nrow=1, align=T, labels=c("A", "B"), label_size=panel.lab.size)

p.sublin.Macrolides.hbarplot.combi

#ggsave(paste0(Figure_output_directory,"SupFig9_TPA-PHE_Sublin-Macrolide-Res.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=160, height=120, device='pdf', dpi=1200)

```
\
\

### Pairwise SNP analysis
\
OK, want to investigate the different patterns observable for the North East of England (pale blue) in Sublineage 1
\
Multiple ways we can do this - including SNP distances (also multiple ways to do that)
\
```{r}
###
#Use phylogenetic distance from the SNP scaled tree
TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist <- ape::cophenetic.phylo(TPA.pyjar.tree.subset.uk)
TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt <- data.frame(Taxa1=row.names(TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist), TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist, stringsAsFactors = F) %>% tidyr::gather(Taxa2, Distance.Phylo, -Taxa1)
# Taxa Comparisons label
TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt$Taxa_combination <- sapply(1:nrow(TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt), function (x) paste0(sort(c(as.character(TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt$Taxa1[x]),as.character(TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt$Taxa2[x]))),collapse="___"))
# Merge together
#TPA.WGS.alignment.data.dist.melt <- dplyr::left_join(TPA.WGS.alignment.data.dist.melt, TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt[,c("Taxa_combination","Distance.Phylo")], by="Taxa_combination")

TPA.WGS.alignment.data.dist.melt <- TPA.pyjar.tree.subset.uk.cophenetic.SNP.dist.melt


TPA.WGS.alignment.data.dist.melt <- unique(TPA.WGS.alignment.data.dist.melt)
```
\
Ok, now bring in some metadata and comparisons
```{r}
# Bring in and merge metadata
PHE.meta.pairwise.t1 <- PHE.metadata.linked[,c("Sample_Name","year","phe_centre","london","gender_orientation","hivpos","age_group","ukborn","TPA.pinecone.sublineage", "TPA_Lineage","Geo_Country","is.UK","is.PHE", "Sample_Year","date.decimal")]

colnames(PHE.meta.pairwise.t1) <- paste0(colnames(PHE.meta.pairwise.t1),".t1")
colnames(PHE.meta.pairwise.t1)[1] <- "Taxa1"
PHE.meta.pairwise.t2 <- PHE.metadata.linked[,c("Sample_Name","year","phe_centre","london","gender_orientation","hivpos","age_group","ukborn","TPA.pinecone.sublineage", "TPA_Lineage","Geo_Country","is.UK","is.PHE", "Sample_Year","date.decimal")]
colnames(PHE.meta.pairwise.t2) <- paste0(colnames(PHE.meta.pairwise.t2),".t2")
colnames(PHE.meta.pairwise.t2)[1] <- "Taxa2"

PHE.alignment.data.dist.melt.meta <- plyr::join(TPA.WGS.alignment.data.dist.melt,PHE.meta.pairwise.t1, by="Taxa1", type="left") 
PHE.alignment.data.dist.melt.meta <- plyr::join(PHE.alignment.data.dist.melt.meta,PHE.meta.pairwise.t2, by="Taxa2", type="left")

# Exclude missing data (e.g. missing sublineage) - this will also remove non-UK samples, since full metadata is missing here
PHE.alignment.data.dist.melt.meta <- PHE.alignment.data.dist.melt.meta[!is.na(PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t1),]
PHE.alignment.data.dist.melt.meta <- PHE.alignment.data.dist.melt.meta[!is.na(PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t2),]

```

\
Define comparisons
```{r}
# Same sample
PHE.alignment.data.dist.melt.meta$same.sample <- ifelse(PHE.alignment.data.dist.melt.meta$Taxa1==PHE.alignment.data.dist.melt.meta$Taxa2,"same", "different")

# Years between samples
PHE.alignment.data.dist.melt.meta$year.distance <- abs(as.numeric(PHE.alignment.data.dist.melt.meta$year.t1) - as.numeric(PHE.alignment.data.dist.melt.meta$year.t2))

PHE.alignment.data.dist.melt.meta$Sample_Year.distance <- abs(as.numeric(PHE.alignment.data.dist.melt.meta$Sample_Year.t1) - as.numeric(PHE.alignment.data.dist.melt.meta$Sample_Year.t2))

# Years between decimal date (more precise temporal distance)
PHE.alignment.data.dist.melt.meta$decimal.date.distance <- abs(as.numeric(PHE.alignment.data.dist.melt.meta$date.decimal.t1) - as.numeric(PHE.alignment.data.dist.melt.meta$date.decimal.t2))

# Epidemiological time between - catagorical
PHE.alignment.data.dist.melt.meta$epi.time.distance.cat <- ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<1/12,"month", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=3/12, "quarter", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=6/12, "half year", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=1, "1 year",ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=2, "2 years", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=3, "3 years", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=4, "4 years", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=5, "5 years",  ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=6, "6 years",">6 years")))))))))

PHE.alignment.data.dist.melt.meta$epi.time.distance.cat <- factor(PHE.alignment.data.dist.melt.meta$epi.time.distance.cat, levels=c("month", "quarter","half year","1 year", "2 years", "3 years", "4 years", "5 years", "6 years", ">6 years"))

PHE.alignment.data.dist.melt.meta$epi.time.distance.cat.years <- ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=1, "0", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=2, "1", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=3, "2", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=4, "3", ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=5, "4",  ifelse(PHE.alignment.data.dist.melt.meta$decimal.date.distance<=6, "5",">5"))))))


# Same country
PHE.alignment.data.dist.melt.meta$same.country <- ifelse(PHE.alignment.data.dist.melt.meta$Geo_Country.t1 == PHE.alignment.data.dist.melt.meta$Geo_Country.t2, "same", "different")

# Is UK
PHE.alignment.data.dist.melt.meta$both.uk <- ifelse(PHE.alignment.data.dist.melt.meta$is.UK.t1 == PHE.alignment.data.dist.melt.meta$is.UK.t2, "same", "different")

# Is PHE
PHE.alignment.data.dist.melt.meta$both.PHE <- ifelse(PHE.alignment.data.dist.melt.meta$is.PHE.t1 == PHE.alignment.data.dist.melt.meta$is.PHE.t2, "same", "different")

# Same TPA Lineage (cleaned up classifications)
PHE.alignment.data.dist.melt.meta$same.TPA.Lineage <- ifelse(PHE.alignment.data.dist.melt.meta$TPA_Lineage.t1==PHE.alignment.data.dist.melt.meta$TPA_Lineage.t2, "same", "different")
PHE.alignment.data.dist.melt.meta$same.TPA.Lineage <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function(x) ifelse((PHE.alignment.data.dist.melt.meta$TPA_Lineage.t1[x]=="0" | PHE.alignment.data.dist.melt.meta$TPA_Lineage.t2[x]=="0"),NA,PHE.alignment.data.dist.melt.meta$same.TPA.Lineage[x]))

# Same TPA sublineage
PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster <- ifelse(PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t1==PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t2,"same", "different")
PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function(x) ifelse(((PHE.alignment.data.dist.melt.meta$same.sample[x]=="different" & PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t1[x]=="Singleton") |(PHE.alignment.data.dist.melt.meta$same.sample[x]=="different" & PHE.alignment.data.dist.melt.meta$TPA.pinecone.sublineage.t2[x]=="Singleton")),"different",PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster[x]))

# Define Genetic relationships hierarchically
PHE.alignment.data.dist.melt.meta$genomic.cluster.hierarchy <- ifelse(PHE.alignment.data.dist.melt.meta$Distance==0,"Zero_SNPs", ifelse(PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster=="same","Same Sublineage", ifelse(PHE.alignment.data.dist.melt.meta$same.TPA.Lineage=="same", "Same Lineage","Different Lineage")))

PHE.alignment.data.dist.melt.meta$genomic.cluster.hierarchy.ph <- ifelse(PHE.alignment.data.dist.melt.meta$Distance.Phylo==0,"Zero_SNPs", ifelse(PHE.alignment.data.dist.melt.meta$same.TPA.Pinecone.cluster=="same","Same Sublineage", ifelse(PHE.alignment.data.dist.melt.meta$same.TPA.Lineage=="same", "Same Lineage","Different Lineage")))


# Same PHE region
PHE.alignment.data.dist.melt.meta$same.PHE.region <- ifelse(PHE.alignment.data.dist.melt.meta$phe_centre.t1==PHE.alignment.data.dist.melt.meta$phe_centre.t2, "same", "different")
PHE.alignment.data.dist.melt.meta$PHE.centre.combination <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function (x) paste0(sort(c(as.character(PHE.alignment.data.dist.melt.meta$phe_centre.t1[x]),as.character(PHE.alignment.data.dist.melt.meta$phe_centre.t2[x]))),collapse="___"))

# does the combination included London?
PHE.alignment.data.dist.melt.meta$involves.London <- ifelse(PHE.alignment.data.dist.melt.meta$phe_centre.t1=="London" | PHE.alignment.data.dist.melt.meta$phe_centre.t2=="London", "London", "not-London")


# Orientation pair
PHE.alignment.data.dist.melt.meta$Orientation_combination <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function (x) paste0(sort(c(as.character(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]),as.character(PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]))),collapse="___"))

#PHE.alignment.data.dist.melt.meta$Orientation.Class <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function (x) ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="MSM" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="MSM", "MSM",
#       ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="MSM" | PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="MSM", "Mixed", 
#              ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="MSW" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="WSM","Heterosexual", 
#                     ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="WSM" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="MSW","Heterosexual","Unknown")))))

PHE.alignment.data.dist.melt.meta$Orientation.Class <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta), function (x) ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="GBMSM" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x]=="GBMSM", "GBMSM",
                                                                                                                             ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x] %in% c("MSW","WSM") & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x] %in% c("MSW","WSM"),"Heterosexual",
                                                                                                                                    ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="GBMSM" & PHE.alignment.data.dist.melt.meta$gender_orientation.t2[x] %in% c("MSW","WSM"), "Mixed", 
                                                                                                                                           ifelse(PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x] %in% c("MSW","WSM") & PHE.alignment.data.dist.melt.meta$gender_orientation.t1[x]=="GBMSM", "Mixed", "Unknown")))))
                    


# Country Comparisons label
PHE.alignment.data.dist.melt.meta$Country_combinations <- paste0(PHE.alignment.data.dist.melt.meta$Geo_Country.t1,"___",PHE.alignment.data.dist.melt.meta$Geo_Country.t2)

# Subset to PHE data only (effectively already done, but let's be explicit)
PHE.TPA.alignment.data.dist.melt.meta <- PHE.alignment.data.dist.melt.meta[(PHE.alignment.data.dist.melt.meta$both.uk=="same" &  PHE.alignment.data.dist.melt.meta$both.PHE=="same"),]
PHE.TPA.alignment.data.dist.melt.meta <- PHE.TPA.alignment.data.dist.melt.meta[PHE.TPA.alignment.data.dist.melt.meta$PHE.only=="PHE",]

PHE.TPA.alignment.data.dist.melt.meta <- PHE.alignment.data.dist.melt.meta[(PHE.alignment.data.dist.melt.meta$both.uk=="same"),]

```
\
\

```{r}
# Make single sided
PHE.TPA.alignment.data.dist.melt.meta <- PHE.TPA.alignment.data.dist.melt.meta[!duplicated(PHE.TPA.alignment.data.dist.melt.meta$Taxa_combination),]

```


\
\
### Perform a more detailed analysis of samples from the North East of England
\
Do a more detailed exploration of the North East of England
\
```{r, fig.height=3, fig.width=4}
PHE.metadata.linked2.region_NorthEast <- PHE.metadata.linked[PHE.metadata.linked$phe_centre=="North East",]

# Constrain by samples being from the North East
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta[(PHE.alignment.data.dist.melt.meta$phe_centre.t1=="North East" & PHE.alignment.data.dist.melt.meta$same.sample=="different"),]

# Constrain by the same PHE region
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[PHE.alignment.data.dist.melt.meta.NorthEast.clusters$same.PHE.region=="same",]

#Just plot these distros
p.NorthEast.Pairwise.SNPs.unconstrained <- ggplot(PHE.alignment.data.dist.melt.meta.NorthEast.clusters, aes(Distance.Phylo)) + 
  geom_histogram(binwidth = 1) +
  theme_bw() +
  theme.text.size +
  labs(x="Pairwise SNP Distance", y="Comparison Count")

p.NorthEast.Pairwise.SNPs.unconstrained
```

\
Make a single linkage network from the North East samples
```{r}

# Constrain by SNP distance (looser than previously - we just want to find basic groupings within sublineage 1 for NE samples)
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[PHE.alignment.data.dist.melt.meta.NorthEast.clusters$Distance.Phylo<=2,]

# And make sure that we actually have genetic distance data for all samples within the network
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[!is.na(PHE.alignment.data.dist.melt.meta.NorthEast.clusters$Distance.Phylo),]

# cleanup some data noise
PHE.alignment.data.dist.melt.meta.NorthEast.clusters <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[!is.na(PHE.alignment.data.dist.melt.meta.NorthEast.clusters$year.t1),]

# prepare intput data (with edge info)
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1 <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters[,c("Taxa1","Taxa2","Distance.Phylo","decimal.date.distance","year.distance","Orientation.Class","epi.time.distance.cat")]

############
# some issues with update to R4 - double sided matrix
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$edgename <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1), function(x) paste0(sort(as.character(unlist(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1[x,c("Taxa1","Taxa2")]))),collapse="___"))
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1 <- PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1[!duplicated(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$edgename),]

# Also having an issue with taxa as factors here
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Taxa1 <- as.character(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Taxa1)
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Taxa2 <- as.character(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Taxa2)
############

#inverse weight
PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Distance.inv <- 1/PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1$Distance.Phylo

# Make actual network
set.seed(1235)
PHE.NorthEast.network <- network(PHE.alignment.data.dist.melt.meta.NorthEast.clusters.input1, matrix.type = "edgelist", ignore.eval = FALSE, directed = F)

PHE.NorthEast.network.gg <- ggnetwork(PHE.NorthEast.network, layout = "kamadakawai", weights = "Distance.inv")
PHE.NorthEast.network.gg$Taxa1 <- PHE.NorthEast.network.gg$vertex.names

# extract temporal clusters from network
PHE.NorthEast.network.ig <- asIgraph(PHE.NorthEast.network)
PHE.NorthEast.network.components <- data.frame(Taxa1=network.vertex.names(PHE.NorthEast.network), vertex.no=as.vector(V(PHE.NorthEast.network.ig)), cluster=igraph::components(PHE.NorthEast.network.ig)$membership)
# For ease of story telling in the paper, flip clusters 2 and 3 around (so we can talk about 2 first)
PHE.NorthEast.network.components <- PHE.NorthEast.network.components %>%
  dplyr::mutate(cluster.old=cluster, cluster=ifelse(cluster.old==2, 3, ifelse(cluster.old==3,2,cluster.old)))
PHE.NorthEast.network.components$Cluster <- paste0("Cluster",PHE.NorthEast.network.components$cluster)

# merge metadata back in
PHE.NorthEast.network.gg <- plyr::join(PHE.NorthEast.network.gg, data.frame(Taxa1=PHE.metadata.linked$Sample_Name, PHE.metadata.linked[,c("phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], stringsAsFactors = F),by="Taxa1", type="left")

PHE.NorthEast.network.gg <- plyr::join(PHE.NorthEast.network.gg, data.frame(Taxa1=PHE.NorthEast.network.components$Taxa1, Cluster=PHE.NorthEast.network.components$Cluster), by="Taxa1", type="left")

```
\
Plot network
```{r}
# Plot network
p.PHE.NorthEast.network.2SNP <- ggplot(PHE.NorthEast.network.gg, aes(x = x, y = y, xend = xend, yend = yend)) + 
  geom_edges(alpha=0.90, curvature = 0.2, aes(color=factor(Distance.Phylo), linetype=factor(Distance.Phylo))) +
  scale_color_manual(values=c("grey5","grey55","grey85"), name="SNP\nDistance") +
  scale_linetype(name="SNP\nDistance") +
  theme_blank() +
  ggnewscale::new_scale_color() + ggnewscale::new_scale("size") +
  geom_nodelabel(aes(color=gender_orientation, label=paste(Taxa1,year,sep="\n"),fontface = "bold"), alpha=0.8, size=theme.text.size.within-0.4, label.size=0.15, label.padding = unit(0.05, "lines")) +
  geom_nodes(size=1.0, aes(color=gender_orientation)) + 
  scale_color_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) + 
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
  NULL
p.PHE.NorthEast.network.2SNP

```


\
Ok, so three networks. Clear differentiation of a heterosexual network (with 0-snp distances) and two predominantly MSM networks
\
Let's look at the phylogenetic context of those North East clusters we've defined.
Pull out subtrees (from sublineage 1 subtree)
\
```{r, fig.height=12, fig.width=12}
# Cluster 1
Beast.tree.NE.cluster1 <- getMRCA(full.beast2.tree@phylo, PHE.NorthEast.network.components[PHE.NorthEast.network.components$Cluster=="Cluster1","Taxa1"])
Beast.tree.NE.cluster1.subtree <- tree_subset(full.beast2.tree, node=Beast.tree.NE.cluster1, levels_back=0)

p.Beast.tree.NE.cluster1.subtree <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Beast.tree.NE.cluster1.subtree, TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, initial.track.offset = 10)

# Can't fit in tip labs, but since this is a polyphyletic subtree, it would be helpful to add a track to highlight the NE strains
PHE.metadata.linked$is.NorthEast <- ifelse(PHE.metadata.linked$phe_centre=="North East","North East", "Other England")
p.Beast.tree.NE.cluster1.subtree.cluster.highlight <- gheatmap(p.Beast.tree.NE.cluster1.subtree, data.frame(row.names=PHE.metadata.linked$Sample_Name, `North East`=PHE.metadata.linked$is.NorthEast), color=NULL,width=(1/max(p.Beast.tree.NE.cluster1.subtree$data$height)*3), offset=10+(4*5),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) + 
    scale_fill_manual(name="North East\nEngland", values=c("#A6CEE3","grey95"), breaks=c("North East","Other England"), na.value = "white", guide = guide_legend(order = 5)) +
    ggnewscale::new_scale_fill()

# Just confirm the ClusterIDs for this subtree (make sure it doesn't enclose other clusters)
p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID <- gheatmap(p.Beast.tree.NE.cluster1.subtree.cluster.highlight, data.frame(row.names=PHE.NorthEast.network.components$Taxa1, ClusterID=PHE.NorthEast.network.components$Cluster), color=NULL,width=(1/max(p.Beast.tree.NE.cluster1.subtree$data$height)*3), offset=10+(4*6),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) + 
    scale_fill_manual(name="North East\nCluster", values=c("#7fc97f","#beaed4","#fdc086"), breaks=c("Cluster1","Cluster2","Cluster3"), na.value = "white", guide = guide_legend(order = 6)) +
    ggnewscale::new_scale_fill()

# add a bit more room to the x axis
p.Beast.tree.NE.cluster1.subtree.cluster.highlight.x.axis.limits <- ggplot_build(p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID)$layout$panel_scales_x[[1]]$range$range
p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID <- p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID + 
    coord_cartesian(x=c(p.Beast.tree.NE.cluster1.subtree.cluster.highlight.x.axis.limits[1],p.Beast.tree.NE.cluster1.subtree.cluster.highlight.x.axis.limits[2]+4), y=c(-0.5-(length(unique(p.Beast.tree.NE.cluster1.subtree.cluster.highlight$data$label))/15),length(unique(p.Beast.tree.NE.cluster1.subtree.cluster.highlight$data$label))+2)) +
  theme(legend.margin = margin(-0.5,0,0,0, unit="mm"))
#p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID

#######################
# Cluster 2
Beast.tree.NE.cluster2 <- getMRCA(full.beast2.tree@phylo, PHE.NorthEast.network.components[PHE.NorthEast.network.components$Cluster=="Cluster2","Taxa1"])
Beast.tree.NE.cluster2.subtree <- tree_subset(full.beast2.tree, node=Beast.tree.NE.cluster2, levels_back=1)

p.Beast.tree.NE.cluster2.subtree <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Beast.tree.NE.cluster2.subtree, TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, initial.track.offset = 20) + geom_tiplab(size=theme.text.size.within, align=T, offset=5, linesize=0.4)
# Just add ClusterIDs for this subtree to highlight
p.Beast.tree.NE.cluster2.subtree <- gheatmap(p.Beast.tree.NE.cluster2.subtree, data.frame(row.names=PHE.NorthEast.network.components$Taxa1, ClusterID=PHE.NorthEast.network.components$Cluster), color=NULL,width=(1/max(p.Beast.tree.NE.cluster2.subtree$data$height)*3), offset=20+(4*5),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) + 
    scale_fill_manual(name="North East\nCluster", values=c("#7fc97f","#beaed4","#fdc086"), breaks=c("Cluster1","Cluster2","Cluster3"), na.value = "white", guide = guide_legend(order = 5, ncol=2)) +
    ggnewscale::new_scale_fill()
# add a bit more room to the x axis
p.Beast.tree.NE.cluster2.subtree.x.axis.limits <- ggplot_build(p.Beast.tree.NE.cluster2.subtree)$layout$panel_scales_x[[1]]$range$range
p.Beast.tree.NE.cluster2.subtree <- p.Beast.tree.NE.cluster2.subtree + 
    coord_cartesian(x=c(p.Beast.tree.NE.cluster2.subtree.x.axis.limits[1],p.Beast.tree.NE.cluster2.subtree.x.axis.limits[2]+12), y=c(-0.5-(length(unique(p.Beast.tree.NE.cluster2.subtree$data$label))/20)-1,length(unique(p.Beast.tree.NE.cluster2.subtree$data$label))+0.5)) + 
  theme(legend.margin = margin(-0.5,0,0,0, unit="mm"))

#p.Beast.tree.NE.cluster2.subtree

############################
# Cluster 3
Beast.tree.NE.cluster3 <- getMRCA(full.beast2.tree@phylo, PHE.NorthEast.network.components[PHE.NorthEast.network.components$Cluster=="Cluster3","Taxa1"])
Beast.tree.NE.cluster3.subtree <- tree_subset(full.beast2.tree, node=Beast.tree.NE.cluster3, levels_back=1)

p.Beast.tree.NE.cluster3.subtree <- plot_beast_subtree_with_PHE_metadata(plot_beast_subtree_with_HPD(Beast.tree.NE.cluster3.subtree, TPA.meta2.1, PHE.metadata.linked,"2019-06-01"), TPA.meta2.1, PHE.metadata.linked, initial.track.offset = 26) + geom_tiplab(size=theme.text.size.within, align=T, offset=3, linesize=0.4)

# Just add ClusterIDs for this subtree to highlight
p.Beast.tree.NE.cluster3.subtree <- gheatmap(p.Beast.tree.NE.cluster3.subtree, data.frame(row.names=PHE.NorthEast.network.components$Taxa1, ClusterID=PHE.NorthEast.network.components$Cluster), color=NULL,width=(1/max(p.Beast.tree.NE.cluster3.subtree$data$height)*3), offset=26+(4*5),colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) + 
    scale_fill_manual(name="North East\nCluster", values=c("#7fc97f","#beaed4","#fdc086"), breaks=c("Cluster1","Cluster2","Cluster3"), na.value = "white", guide = guide_legend(order = 5, ncol=2)) +
    ggnewscale::new_scale_fill()

# add a bit more room to the x axis
p.Beast.tree.NE.cluster3.subtree.x.axis.limits <- ggplot_build(p.Beast.tree.NE.cluster3.subtree)$layout$panel_scales_x[[1]]$range$range
p.Beast.tree.NE.cluster3.subtree <- p.Beast.tree.NE.cluster3.subtree + 
    coord_cartesian(x=c(p.Beast.tree.NE.cluster3.subtree.x.axis.limits[1],p.Beast.tree.NE.cluster3.subtree.x.axis.limits[2]+12), y=c(-0.5-(length(unique(p.Beast.tree.NE.cluster3.subtree$data$label))/20)-1,length(unique(p.Beast.tree.NE.cluster3.subtree$data$label))+0.5)) + 
  theme(legend.margin = margin(-0.5,0,0,0, unit="mm"))
#p.Beast.tree.NE.cluster3.subtree

#p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID
#p.Beast.tree.NE.cluster2.subtree 
#p.Beast.tree.NE.cluster3.subtree 
```

\
Since Cluster 1 is really quite polyphyletic, it maybe more useful to show the clusters in context for that one

```{r}
# Add North East identifier column
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight <- gheatmap(sublineage.1.tree.heatmap, data.frame(row.names=PHE.metadata.linked$Sample_Name, `North East`=PHE.metadata.linked$is.NorthEast), color=NULL,width=(1/max(sublineage.1.tree.heatmap$data$height)*3)*1.2, offset=0+(4*5)*1.2,colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) + 
    scale_fill_manual(name="North East\nEngland", values=c("#A6CEE3","grey95"), breaks=c("North East","Other England"), na.value = "white", guide = guide_legend(order = 5)) +
    ggnewscale::new_scale_fill()

# Just confirm the ClusterIDs for this subtree (make sure it doesn't enclose other clusters)
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight <- gheatmap(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight, data.frame(row.names=PHE.NorthEast.network.components$Taxa1, ClusterID=PHE.NorthEast.network.components$Cluster), color=NULL,width=(1/max(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight$data$height)*3)*1.2, offset=0+(4*6)*1.2,colnames_angle=-45,colnames_offset_y=0.02, hjust=-0.0, font.size=theme.text.size.within) + 
    scale_fill_manual(name="North East\nCluster", values=c("#7fc97f","#beaed4","#fdc086"), breaks=c("Cluster1","Cluster2","Cluster3"), na.value = "white", guide = guide_legend(order = 6, ncol=2)) +
    ggnewscale::new_scale_fill()

# add a bit more room to the x axis
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight.x.axis.limits <- ggplot_build(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight)$layout$panel_scales_x[[1]]$range$range
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight <- p.Beast.tree.sublineage1.NE.subtree.cluster.highlight + 
    coord_cartesian(x=c(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight.x.axis.limits[1],p.Beast.tree.sublineage1.NE.subtree.cluster.highlight.x.axis.limits[2]+4), y=c(-0.5-(length(unique(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight$data$label))/15),length(unique(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight$data$label))+2))

# reduce spacing between legend scales
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight <- p.Beast.tree.sublineage1.NE.subtree.cluster.highlight + theme(legend.margin = margin(-0.95,0,0,0, unit="mm"))
p.Beast.tree.sublineage1.NE.subtree.cluster.highlight

```


\ 
Plot together
```{r, fig.height=10, fig.width=10}

p.Beast.tree.NE.subtrees.combi1 <- plot_grid(p.Beast.tree.NE.cluster2.subtree, p.Beast.tree.NE.cluster3.subtree, ncol=1, labels=c("C - Cluster 2", "D - Cluster 3"), vjust=1.0, label_size=panel.lab.size, scale=0.95)

p.Beast.tree.NE.subtrees.combi2 <- plot_grid(p.Beast.tree.NE.cluster1.subtree.cluster.highlight.with_clusterID, p.Beast.tree.NE.subtrees.combi1, ncol=2, rel_widths=c(3,2), labels=c("B - Cluster 1", ""), label_size=panel.lab.size)
p.Beast.tree.NE.subtrees.combi2


p.Beast.tree.NE.subtrees.combi3 <- plot_grid(p.Beast.tree.sublineage1.NE.subtree.cluster.highlight, p.Beast.tree.NE.subtrees.combi1, ncol=2, rel_widths=c(8,7), labels=c("B - Sublineage 1 (All)", ""), label_size=panel.lab.size, scale=0.95, vjust=1.0)

p.Beast.tree.NE.subtrees.combi3

```

\
\
Look more closely at population demographics of these clusters
```{r}
# Metadata on NE cluster 2
PHE.metadata.linked %>% 
  dplyr::filter(Sample_Name %in% Beast.tree.NE.cluster2.subtree@phylo$tip.label) %>%
  dplyr::group_by(Geo_Country, is.NorthEast, gender_orientation) %>%
  dplyr::summarise(Count=n())

# Metadata on NE cluster 3
PHE.metadata.linked %>% 
  dplyr::filter(Sample_Name %in% Beast.tree.NE.cluster3.subtree@phylo$tip.label) %>%
  dplyr::group_by(Geo_Country, is.NorthEast, gender_orientation) %>%
  dplyr::summarise(Count=n())

# Country info on NE cluster 3
TPA.meta2.1 %>% 
  dplyr::filter(Sample_Name %in% Beast.tree.NE.cluster3.subtree@phylo$tip.label) %>%
  dplyr::group_by(Geo_Country) %>%
  dplyr::summarise(Count=n())

# Separate metadata records show Hungarian sample "TPA_HUN180001" came from a male bisexual (MSWM).
```
\
Examine SNP scaled tree for distances
```{r}

# Extract information about SNP distances
TPA.NEcluster3.pyjartree.mrca <- getMRCA(TPA.pyjar.tree, as.character(unlist(TPA.meta2.1[TPA.meta2.1$Sample_Name %in% Beast.tree.NE.cluster3.subtree@phylo$tip.label,"Sample_Name"])))


TPA.NEcluster3.pyjartree.subtree <- tree_subset(TPA.pyjar.tree, node=TPA.NEcluster3.pyjartree.mrca, levels_back=1)

ggtree(TPA.NEcluster3.pyjartree.subtree) + geom_tiplab(size=theme.text.size.within)
ggtree(TPA.NEcluster3.pyjartree.subtree)$data
```

\
\
Do some analysis of nearest neighbour and distances to MRCAs
```{r}
calculate.years.from.mrca <- function(current.ggtree.phylo, current.ggtree.data){
  #current.ggtree <- Beast.tree.NE.cluster3.subtree
  all.tips <- current.ggtree.phylo$tip.label
  dist.2.mrca <- NULL
  ### put dates into df
  current.ggtree.data$mrca.median <- 2019.5 - current.ggtree.data$height_median
  current.ggtree.data$year <- as.numeric(round(2019.5 - current.ggtree.data$height_median,3))
  current.ggtree.data$mrca.95high <- round(2019.5 - sapply(1:nrow(current.ggtree.data),function(x) as.numeric(unlist(current.ggtree.data[x,"height_0.95_HPD"]))[1]), 3)
  current.ggtree.data$mrca.95low <- round(2019.5 - sapply(1:nrow(current.ggtree.data),function(x) as.numeric(unlist(current.ggtree.data[x,"height_0.95_HPD"]))[2]), 3)
  # extract dates between sample and its MRCA using loop
  for (current.node in all.tips) {
    current.parent <- c(match(current.node,current.ggtree.phylo$tip.label), phangorn::Ancestors(current.ggtree.phylo, match(c(current.node), current.ggtree.phylo$tip.label), "parent"))
    
    current.nodelist <- current.ggtree.data[current.ggtree.data$node %in% current.parent,]
    current.dist.2.mrca <- c(current.node, as.numeric(current.nodelist[1,"year"]-current.nodelist[2,"year"]))
    dist.2.mrca <- rbind(dist.2.mrca, current.dist.2.mrca)
  }
  dist.2.mrca <- data.frame(Sample_Name=as.character(dist.2.mrca[,1]), dist.to.mrca=as.numeric(dist.2.mrca[,2]), stringsAsFactors=F)
  return(dist.2.mrca)
}

### All samples in global tree
dist.mrca.all.TPA <- calculate.years.from.mrca(full.beast2.tree@phylo, full.beast2.tree@data)

```
\
Merge dist2MRCA with metadata
```{r}
PHE.metadata.linked.dist2mrca <- left_join(PHE.metadata.linked, dist.mrca.all.TPA, by="Sample_Name")

p.time2mrca.orientation <- ggplot(PHE.metadata.linked.dist2mrca, aes(gender_orientation, dist.to.mrca, color=gender_orientation)) + 
  geom_quasirandom(size=0.75, alpha=0.5) +
  theme_light() + theme.text.size +
  coord_flip() +
  labs(x="Gender Orientation", y="Years to MRCA", color="Gender Orientation") +
  theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
  scale_color_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation)

p.time2mrca.phe_region <- ggplot(PHE.metadata.linked.dist2mrca, aes(phe_centre, dist.to.mrca, color=phe_centre)) + 
  geom_quasirandom(size=0.75, alpha=0.5) +
  theme_light() + theme.text.size +
  coord_flip(ylim=c(0,40)) +
  labs(x="UKHSA Region", y="Years to MRCA", color="UKHSA Region") +
  theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
  scale_color_manual(name="UKHSA\nRegion", values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region)

p.time2mrca.phe_region.orientation <- ggplot(PHE.metadata.linked.dist2mrca, aes(phe_centre, dist.to.mrca, color=gender_orientation)) + 
  geom_quasirandom(size=0.75, alpha=0.5) +
  theme_light() + theme.text.size +
  coord_flip(ylim=c(0,20)) +
  labs(x="UKHSA Region", y="Years to MRCA") +
  theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
  scale_color_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation)
p.time2mrca.phe_region.orientation


p.time2mrca.sublineage <- ggplot(PHE.metadata.linked.dist2mrca, aes(TPA.pinecone.sublineage, dist.to.mrca, color=TPA.pinecone.sublineage)) + 
  geom_quasirandom(size=0.75, alpha=0.5) +
  theme_light() + theme.text.size +
  coord_flip() +
  labs(x="TPA Lineage", y="Years to MRCA", color="TPA Lineage") +
  theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
  scale_color_manual(values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage)
p.time2mrca.sublineage


p.time2mrca.Lineage <- ggplot(PHE.metadata.linked.dist2mrca, aes(TPA_Lineage, dist.to.mrca, color=TPA_Lineage)) + 
  geom_quasirandom(size=0.75, alpha=0.5) +
  theme_light() + theme.text.size +
  coord_flip() +
  labs(x="TPA Lineage", y="Years to MRCA (Median of Posterior)", color="TPA Lineage") +
  theme(legend.position='bottom', legend.key.size = unit(0.55,"line")) +
  scale_color_manual(values=TPA_Lineage.cols$Lineage.col, breaks=TPA_Lineage.cols$Lineage)
```
\
\
Maybe can make an MST of the North East samples for grapetree?
```{r}
TPA.pyjar.tree.subset.NorthEast <- ape::keep.tip(TPA.pyjar.tree, as.character(unlist(PHE.metadata.linked[PHE.metadata.linked$phe_centre=="North East","Sample_Name"])))

#ggtree(TPA.pyjar.tree.subset.NorthEast)
#write.tree(TPA.pyjar.tree.subset.NorthEast, paste0(Data_input_directory,"TPA.UK-only-NorthEast.pyjar.2022-02-26.tre"))

# Write out a metadata sheet for the relevant information
PHE.metadata.linked.grapetree <- PHE.metadata.linked[,c("Sample_Name", "year","gender_orientation","phe_centre","hivpos","ukborn","TPA_Lineage","TPA.pinecone.sublineage")]
colnames(PHE.metadata.linked.grapetree)[1] <- "ID"

#write.table(PHE.metadata.linked.grapetree, paste0(Data_input_directory,"TPA.UK-only.grapetree.meta.2022-02-03.tsv"), sep = "\t", quote=F, row.names = F)
```


Alternative approach using MST instead of networks for North East data
```{r}
# Read in MST
#TPA.NorthEastEngland.Grapetree.file <- paste0(Data_input_directory,"TPA-UK-NorthEast-2022-02-26.GenderOrientation-MSTree.inkscaped.+node-counts+GBMSM.svg")

p.TPA.NorthEastEngland.Grapetree <- ggdraw() + draw_image(TPA.NorthEastEngland.Grapetree.file)
p.TPA.NorthEastEngland.Grapetree

p.TPA.NorthEastEngland.Grapetree.header <- plot_grid(p.TPA.NorthEastEngland.Grapetree, labels=c("A - Network Clusters (North East England)"), label_size=panel.lab.size, scale=0.95)

```
\
Plot with beast trees
```{r, fig.height=12, fig.width=12}
#p.PHE.NorthEast_MST.with.beast.subtrees.combi <- plot_grid(p.TPA.NorthEastEngland.Grapetree, p.Beast.tree.NE.subtrees.combi3, ncol=1, rel_heights=c(3,6), labels=c("A - Network Clusters (North East England)", ""), label_size=panel.lab.size, scale = 0.95)

p.PHE.NorthEast_MST.with.beast.subtrees.combi <- plot_grid(p.TPA.NorthEastEngland.Grapetree.header, p.Beast.tree.NE.subtrees.combi3, ncol=1, rel_heights=c(3,7))



p.PHE.NorthEast_MST.with.beast.subtrees.combi
#ggsave(paste0(Figure_output_directory,"Fig3_Sublin1.NorthEast.MST+Beast.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=200, height=245, device='pdf', dpi=1200)


```

\
Do some analysis of major sublineages over time by region - could this influence observations about sublineages?
```{r, fig.height=6, fig.width=4}
# Generate some stats by PHE Region
PHE.major.sublineage.PHEcentre.date <- PHE.metadata.linked %>% 
  dplyr::filter(TPA.pinecone.sublineage %in% c(1,14)) %>%
  dplyr::group_by(TPA.pinecone.sublineage, phe_centre, year) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.sublin=sum(Count)) %>%
  dplyr::arrange(desc(phe_centre), .by_group=T) %>%
  dplyr::mutate(fraction=Count/total.sublin, cum_fract=cumsum(fraction), cum_fract.mid = cum_fract-(fraction/2))


ggplot(PHE.major.sublineage.PHEcentre.date, aes(year, phe_centre, size=Count, color=TPA.pinecone.sublineage)) +
  geom_point() + 
  facet_grid(.~TPA.pinecone.sublineage) +
  theme_light() +
  theme.text.size +
  scale_color_manual(values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage)


p.PHE.major.sublineage.PHEcentre.date.bubbleplot <- ggplot(PHE.major.sublineage.PHEcentre.date, aes(year, TPA.pinecone.sublineage, color=TPA.pinecone.sublineage)) +
  geom_point(alpha=0.65, aes(size=Count)) + 
  geom_line(alpha=0.25) +
  facet_grid(factor(gsub("\\ ","\n",phe_centre), levels=gsub("\\ ","\n",PHE.region.cols.brew$UKHSA.region))~., switch='y') +
  theme_light() +
  theme(strip.placement = "outside") +
  theme(strip.background = element_rect(color='white', fill='white',linetype="solid"), strip.text.y=element_text(color = "grey25",angle=0, size=5)) + 
  scale_size_area(max_size = 4.5,breaks=c(1,5,10,20,30,40)) +
  theme.text.size +
  scale_color_manual(values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage) + 
  labs(y="Region", x="Year", color="Sublineage") 
 
p.PHE.major.sublineage.PHEcentre.date.bubbleplot

```
\
Do some specific analysis for the 3 Northern regions
```{r, fig.width=4, fig.height=3}
# Generate some stats by PHE Region
 PHE.metadata.linked %>% 
  dplyr::filter(phe_centre %in% c("North East", "North West", "Yorkshire and Humber")) %>%
  dplyr::summarise(count=n())

 PHE.metadata.linked %>% 
  dplyr::filter(phe_centre %in% c("North East", "North West", "Yorkshire and Humber")) %>%
  dplyr::group_by(year) %>%
  dplyr::summarise(count=n())


p.PHE.major.sublineage.3NorthernRegions <- PHE.metadata.linked %>% 
  dplyr::filter(phe_centre %in% c("North East", "North West", "Yorkshire and Humber")) %>%
  dplyr::group_by(TPA.pinecone.sublineage, year, phe_centre) %>%
  dplyr::summarise(Count=n()) %>%
  ggplot(aes(year, Count, fill=phe_centre)) + 
  geom_bar(stat='identity', width=0.65) +
  scale_fill_manual(values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region) +
  theme_bw() + theme.text.size +
  scale_x_continuous(breaks=seq(2012,2018,1)) +
  scale_y_continuous(breaks=pretty) +
  labs(title="Samples in 3 Northern Regions", x="Collection Year", y="Sample Count", fill="Public Health\nRegion") +
  theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
  #geom_text(aes(x=year,y=Count-0.5, label=Count), color='grey95', size=theme.text.size.within) +
  NULL
p.PHE.major.sublineage.3NorthernRegions

```



\
Single linkage network of identical genomes from UK

```{r}
# Constrain by SNP distance (identical in the asr snp tree)
PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta[PHE.alignment.data.dist.melt.meta$Distance.Phylo==0,]

# and a max of 2 years
#PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta.identicals[PHE.alignment.data.dist.melt.meta.identicals$decimal.date.distance<=2,]


# And make sure that we actually have genetic distance data for all samples within the network
PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta.identicals[!is.na(PHE.alignment.data.dist.melt.meta.identicals$Distance.Phylo),]

# remove self-samples
PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta.identicals[PHE.alignment.data.dist.melt.meta.identicals$same.sample=="different",]


# cleanup some data noise
PHE.alignment.data.dist.melt.meta.identicals <- PHE.alignment.data.dist.melt.meta.identicals[!is.na(PHE.alignment.data.dist.melt.meta.identicals$year.t1),]

# prepare intput data (with edge info)
PHE.alignment.data.dist.melt.meta.identicals.input1 <- PHE.alignment.data.dist.melt.meta.identicals[,c("Taxa1","Taxa2","Distance.Phylo","decimal.date.distance","year.distance","Orientation.Class","epi.time.distance.cat.years","epi.time.distance.cat")]

############
# some issues with update to R4 - double sided matrix
PHE.alignment.data.dist.melt.meta.identicals.input1$edgename <- sapply(1:nrow(PHE.alignment.data.dist.melt.meta.identicals.input1), function(x) paste0(sort(as.character(unlist(PHE.alignment.data.dist.melt.meta.identicals.input1[x,c("Taxa1","Taxa2")]))),collapse="___"))
PHE.alignment.data.dist.melt.meta.identicals.input1 <- PHE.alignment.data.dist.melt.meta.identicals.input1[!duplicated(PHE.alignment.data.dist.melt.meta.identicals.input1$edgename),]

# Also having an issue with taxa as factors here
PHE.alignment.data.dist.melt.meta.identicals.input1$Taxa1 <- as.character(PHE.alignment.data.dist.melt.meta.identicals.input1$Taxa1)
PHE.alignment.data.dist.melt.meta.identicals.input1$Taxa2 <- as.character(PHE.alignment.data.dist.melt.meta.identicals.input1$Taxa2)
############
# Deduplicate

#inverse weight
PHE.alignment.data.dist.melt.meta.identicals.input1$decimal.date.distance.inv <- 1/1/(PHE.alignment.data.dist.melt.meta.identicals.input1$decimal.date.distance+0.04)

# Make actual network
set.seed(1236)
PHE.identicals.network <- network(PHE.alignment.data.dist.melt.meta.identicals.input1, matrix.type = "edgelist", ignore.eval = FALSE, directed = F, loops = F)

#PHE.identicals.network.gg <- ggnetwork(PHE.identicals.network, layout = "kamadakawai", weights = "decimal.date.distance.inv")
#PHE.identicals.network.gg <- ggnetwork(PHE.identicals.network, layout = "fruchtermanreingold", weights = "decimal.date.distance")
PHE.identicals.network.gg <- ggnetwork(PHE.identicals.network, layout = "fruchtermanreingold")

PHE.identicals.network.gg$Taxa1 <- PHE.identicals.network.gg$vertex.names

# extract temporal clusters from network
PHE.identicals.network.ig <- asIgraph(PHE.identicals.network)
PHE.identicals.network.components <- data.frame(Taxa1=network.vertex.names(PHE.identicals.network), vertex.no=as.vector(V(PHE.identicals.network.ig)), cluster=igraph::components(PHE.identicals.network.ig)$membership)
PHE.identicals.network.components$Cluster <- paste0("Cluster",PHE.identicals.network.components$cluster)

# merge metadata back in
PHE.identicals.network.gg <- plyr::join(PHE.identicals.network.gg, data.frame(Taxa1=PHE.metadata.linked$Sample_Name, PHE.metadata.linked[,c("phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], stringsAsFactors = F),by="Taxa1", type="left")

PHE.identicals.network.gg <- plyr::join(PHE.identicals.network.gg, data.frame(Taxa1=PHE.identicals.network.components$Taxa1, Cluster=PHE.identicals.network.components$Cluster), by="Taxa1", type="left")


# 
# Add temporal colour scale
#unique(PHE.identicals.network.gg$epi.time.distance.cat)

epi.time.distance.cat.cols <- rev(colorRampPalette(brewer.pal(8, "Greys"))(length(unique(PHE.identicals.network.gg$epi.time.distance.cat))-1))


# Plot network
p.PHE.identicals.network.0SNP <- ggplot(PHE.identicals.network.gg, aes(x = x, y = y, xend = xend, yend = yend)) + 
  geom_edges(alpha=0.90, curvature = 0.2, aes(color=factor(epi.time.distance.cat), linetype=factor(epi.time.distance.cat))) +
  #scale_color_manual(values=c("grey5","grey35","grey55", "grey65", "grey75"), name="SNP\nDistance") +
  scale_color_manual(name="Temporal\nDistance", values = epi.time.distance.cat.cols) +
  scale_linetype(name="Temporal\nDistance") +
  theme_blank() +
  ggnewscale::new_scale_color() + ggnewscale::new_scale("size") +
  #geom_nodelabel(aes(color=gender_orientation, label=paste(Taxa1,year,sep="\n"),fontface = "bold"), alpha=0.8, size=theme.text.size.within-0.4, label.size=0.15, label.padding = unit(0.05, "lines")) +
  geom_nodes(size=2.5, aes(color=gender_orientation), alpha=0.9) + 
  scale_color_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation) + 
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
  NULL
p.PHE.identicals.network.0SNP

```

Plot this against a UK tree?
```{r}
gheatmap(ggtree(TPA.pyjar.tree.subset.uk),
data.frame(row.names=PHE.identicals.network.components$Taxa1, Cluster=PHE.identicals.network.components$Cluster))

```



\
Some stats from this
```{r}
p.PHE.identical.Orientation_class.bydatedist <- PHE.alignment.data.dist.melt.meta %>%
  dplyr::filter(same.sample=="different", Distance.Phylo==0) %>%
  #filter(decimal.date.distance<=1) %>%
  dplyr::group_by(epi.time.distance.cat, Orientation.Class) %>% 
  dplyr::summarise(Count.class.date=n()) %>%
  dplyr::mutate(sum.class=sum(Count.class.date), fract.class=Count.class.date/sum.class) %>%
  ggplot(aes(x=epi.time.distance.cat, y=Count.class.date, fill=Orientation.Class)) +
  geom_bar(stat='identity', position='stack') +
  theme_bw() +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
  labs(x="Time between samples", y="Interaction Count", fill="Orientation Type")
p.PHE.identical.Orientation_class.bydatedist


  
p.PHE.identical.Orientation_class.byZerodist.cluster <- PHE.identicals.network.gg %>%
  dplyr::filter(!is.na(Orientation.Class)) %>%
  dplyr::group_by(Cluster, Orientation.Class) %>% 
  dplyr::summarise(Count.class.cluster=n()) %>%
  dplyr::mutate(sum.class=sum(Count.class.cluster), fract.class=Count.class.cluster/sum.class) %>%
  dplyr::arrange(desc(sum.class)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(Cluster=as_factor(Cluster)) %>%
  ggplot(aes(x=Cluster, y=Count.class.cluster, fill=Orientation.Class)) +
  geom_bar(stat='identity', position='stack') + 
  theme_bw() +
  x.theme.axis.rotate +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
  labs(x="Identical Genome Cluster", y="Interaction Count", fill="Orientation Type")
p.PHE.identical.Orientation_class.byZerodist.cluster

d.PHE.identical.GenderOrientation.byZerodist.cluster <- left_join(PHE.identicals.network.components[,c("Taxa1","Cluster")], PHE.metadata.linked[,c("Sample_Name","phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], by=c("Taxa1"="Sample_Name")) %>%
  dplyr::group_by(TPA.pinecone.sublineage, Cluster, gender_orientation) %>%
  dplyr::summarise(count.orient.cluster=n()) %>%
  dplyr::mutate(count.cluster=sum(count.orient.cluster), fract=count.orient.cluster/count.cluster) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(desc(count.cluster)) %>%
  dplyr::mutate(Cluster.o=as_factor(Cluster))


# Plot sample counts by genome cluster (coloured by orientation)
p.PHE.identical.GenderOrientation.byZerodist.cluster <- d.PHE.identical.GenderOrientation.byZerodist.cluster %>%
  ggplot(aes(Cluster.o, count.orient.cluster, fill=gender_orientation)) + 
  geom_bar(stat="identity", width=0.65) +
  scale_fill_manual(name="Gender\nOrientation", values=PHE.orientation.cols$orientation.cols, breaks=PHE.orientation.cols$orientation, guide = guide_legend(order = 1)) +
  theme_light() +
  x.theme.axis.rotate + 
  scale_y_continuous(breaks=seq(0,45,5)) +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
  labs(x="Identical Genome Cluster", y="Sample Count", fill="Patient Gender Orientation") 

# Add details of sublineage  
p.PHE.identical.GenderOrientation.byZerodist.cluster <- p.PHE.identical.GenderOrientation.byZerodist.cluster + 
  ggnewscale::new_scale_color() +
  geom_point(data=(d.PHE.identical.GenderOrientation.byZerodist.cluster %>% select(Cluster.o, TPA.pinecone.sublineage) %>% distinct()), aes(Cluster.o, -1.5, color=TPA.pinecone.sublineage), inherit.aes = F) + scale_color_manual(values=sublineages.cols.brew$sublineage.cols, breaks=sublineages.cols.brew$sublineage, name="Sublineage", guide = guide_legend(order = 2)) +
  NULL

# Add a sublineage axis label (bit of a hack)
p.PHE.identical.GenderOrientation.byZerodist.cluster <- p.PHE.identical.GenderOrientation.byZerodist.cluster + 
  geom_text(data=data.frame(lab="Sublineage", y=-1.5, x=28, stringsAsFactors=F), aes(label=lab, x=x, y=y), hjust = 0.1, size=theme.text.size.within, inherit.aes = F) +
  coord_cartesian(x=c(1, 27), clip='off')
  
p.PHE.identical.GenderOrientation.byZerodist.cluster

######gxxxxgsave(paste0(Figure_output_directory,"SupFig6_Identical-SNP-clust_orientation.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=120, height=100, device='pdf', dpi=1200)

```
\
Possible to introduce some more info into that plot?


```{r}
d.PHE.identical.region.byZerodist.cluster <- left_join(PHE.identicals.network.components[,c("Taxa1","Cluster")], PHE.metadata.linked[,c("Sample_Name","phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], by=c("Taxa1"="Sample_Name")) %>%
  dplyr::group_by(TPA.pinecone.sublineage, Cluster, phe_centre) %>%
  dplyr::summarise(count.region.cluster=n()) %>%
  dplyr::mutate(count.cluster=sum(count.region.cluster), fract=count.region.cluster/count.cluster) %>%
  dplyr::ungroup() %>%
  dplyr::arrange(desc(count.cluster)) %>%
  dplyr::mutate(Cluster.o=as_factor(Cluster))


p.PHE.identical.Region.byZerodist.cluster <- d.PHE.identical.region.byZerodist.cluster %>%
  ggplot(aes(Cluster.o, count.region.cluster, fill=phe_centre)) + 
  geom_bar(stat="identity", width=0.65, position='fill') +
  scale_fill_manual(name="UKHSA\nRegion", values=PHE.region.cols.brew$region.col, breaks=PHE.region.cols.brew$UKHSA.region, guide = guide_legend(order = 1)) +
  theme_light() +
  x.theme.axis.rotate + 
  scale_y_continuous(breaks=seq(0,45,5)) +
  theme.text.size + theme(legend.key.size = unit(0.55,"line"),legend.position='right') +
  guides(fill=guide_legend(ncol=2)) +
  labs(x="Identical Genome Cluster", y="Region Proportion", fill="UKHSA Region") 


```

```{r, fig.height=8, fig.width=8}
p.PHE.identical.byZerodist.cluster.barcombi <- plot_grid(p.PHE.identical.GenderOrientation.byZerodist.cluster + x.theme.strip, p.PHE.identical.Region.byZerodist.cluster, ncol=1, axis="rlt", align=T, rel_heights = c(2,1), labels=c("B","C"), label_size=panel.lab.size)

#p.PHE.identical.byZerodist.cluster.barcombi
#p.PHE.identicals.network.0SNP

plot_grid(p.PHE.identicals.network.0SNP, p.PHE.identical.byZerodist.cluster.barcombi, ncol=1, rel_heights=c(2,3), labels=c("A",""), label_size=panel.lab.size)


p.PHE.identical.byZerodist.cluster.barcombi.noNet <- plot_grid(p.PHE.identical.GenderOrientation.byZerodist.cluster + x.theme.strip, p.PHE.identical.Region.byZerodist.cluster, ncol=1, axis="rlt", align=T, rel_heights = c(2,1), labels=c("A","B"), label_size=panel.lab.size)
p.PHE.identical.byZerodist.cluster.barcombi.noNet 


#ggsave(paste0(Figure_output_directory,"SupFig6_Identical-SNP-clust_orientation.",format(Sys.Date(),"%Y%m%d"),".pdf"), units='mm', width=120, height=120, device='pdf', dpi=1200)
```



```{r}
PHE.identicals.network.gg.region.scatterpie.groups <- PHE.identicals.network.gg %>%
  dplyr::select(Cluster, Taxa1, phe_centre) %>%
  dplyr::distinct() %>%
  dplyr::group_by(Cluster, phe_centre) %>% 
  dplyr::summarise(Count.centre=n()) %>%
  dplyr::mutate(x=Cluster, y=3.5) %>%
  pivot_wider(names_from="phe_centre", values_from="Count.centre", values_fill=0) %>%
  dplyr::select(Cluster,x,y,unique(PHE.identicals.network.gg$phe_centre)) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(Cluster.numeric=as.numeric(1:27))
  

p.PHE.identical.GenderOrientation.byZerodist.cluster + 
  ggnewscale::new_scale_fill() #+
  

```



\
Get a few more stats on the largest cluster (Cluster 8)
```{r}
#d.PHE.identical.GenderOrientation.byZerodist.cluster %>% filter(Cluster=="Cluster8")

PHE.identicals.network.gg.identical.cluster8 <- PHE.identicals.network.gg %>% filter(Cluster=="Cluster8")  %>%
  select(vertex.names, Orientation.Class, phe_centre, year, TPA_Lineage, TPA.pinecone.sublineage, hivpos, Cluster)

sort(unique(PHE.identicals.network.gg.identical.cluster8$year))

```

\
Get some more information about the heterosexual only clusters
```{r}
PHE.identicals.network.gg.identical_heteroclusters <- PHE.identicals.network.gg %>% filter(Cluster %in% c("Cluster12", "Cluster20", "Cluster27"))  %>%
  select(vertex.names, Cluster, gender_orientation, phe_centre, year, TPA_Lineage, TPA.pinecone.sublineage, hivpos) %>% 
  distinct() %>%
  arrange(Cluster, year, gender_orientation)

PHE.identicals.network.gg.identical_heteroclusters
```
\ 
And do the same for the small mixed/GBMSM clusters
```{r}
PHE.identicals.network.gg.identical_not.heteroclusters <- PHE.identicals.network.gg %>% filter(Cluster %notin% c("Cluster12", "Cluster20", "Cluster27", "Cluster8"))  %>%
  select(vertex.names, Cluster, gender_orientation, phe_centre, year, TPA_Lineage, TPA.pinecone.sublineage, hivpos) %>% 
  distinct() %>%
  arrange(Cluster, year, gender_orientation)
PHE.identicals.network.gg.identical_not.heteroclusters
```



What proportion of heterosexuals have an identical GBMSM paired genome?
\
```{r}

# Delineate heterosexual clusters
d.PHE.identical.heterosexual.clusters <- d.PHE.identical.GenderOrientation.byZerodist.cluster %>% 
  dplyr::mutate(is.heterosexual=ifelse(gender_orientation%in% c("MSW", "WSM"), "heterosexual", ifelse(gender_orientation=="GBMSM","GBMSM", "Unknown"))) %>%
  dplyr::group_by(Cluster,is.heterosexual) %>% 
  dplyr::mutate(count.hetero=sum(count.orient.cluster), fract.hetero=sum(count.orient.cluster)/count.cluster) %>%
  dplyr::ungroup() %>%
  dplyr::filter(is.heterosexual=="heterosexual") %>% 
  dplyr::select(-c(count.orient.cluster, gender_orientation, fract)) %>%
  dplyr::distinct() %>%
  dplyr::mutate(cluster.type=ifelse(fract.hetero==1, "hetero.only", "other"))

d.PHE.identical.heterosexual.clusters 

# What proportion of heterosexuals (n=20) are in a heterosexual-only cluster?
d.PHE.identical.heterosexual.clusters %>% 
  dplyr::group_by(cluster.type) %>%
  dplyr::summarise(count.in.hetero.cluster=sum(count.hetero)) %>% 
  dplyr::mutate(fract.in.hetero=count.in.hetero.cluster/sum(count.in.hetero.cluster))
  

#left_join(PHE.identicals.network.components[,c("Taxa1","Cluster")], PHE.metadata.linked[,c("Sample_Name","phe_centre","london","year","age_group","ukborn","gender_orientation","hivpos","TPA.pinecone.sublineage","TPA_Lineage")], by=c("Taxa1"="Sample_Name"))
```

\


# Revisions 03-2023 onwards

\
Look at proportion of genomes at different coverage thresholds
```{r}
# Cumulative proportion of N counts in genomes
PHE.metadata.Ncount.cummulative.UK <- PHE.metadata.linked %>% 
  dplyr::filter(is.UK=="UK") %>%
  dplyr::group_by(`Proportion-N_>5_mapping+masking_Nichols`) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.Count=sum(Count)) %>%
  dplyr::mutate(fraction=Count/total.Count, cum_fract=cumsum(fraction), cum_count=cumsum(Count)) %>%
  dplyr::mutate(Dataset="UK (n=237)")
PHE.metadata.Ncount.cummulative.UK


PHE.metadata.Ncount.cummulative.ALL <- TPA.meta2.1 %>% 
  dplyr::filter(full.temporal.analysis=="Yes") %>%
  dplyr::group_by(`Proportion-N_>5_mapping+masking_Nichols`) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.Count=sum(Count)) %>%
  dplyr::mutate(fraction=Count/total.Count, cum_fract=cumsum(fraction), cum_count=cumsum(Count)) %>%
  dplyr::mutate(Dataset="All (n=520)") 
PHE.metadata.Ncount.cummulative.ALL
PHE.metadata.Ncount.cummulative.combi <- rbind(PHE.metadata.Ncount.cummulative.UK, PHE.metadata.Ncount.cummulative.ALL)




p.cumulative.Ncount.for.datset <-  ggplot(PHE.metadata.Ncount.cummulative.combi , aes(`Proportion-N_>5_mapping+masking_Nichols`, cum_fract, group=Dataset, color=Dataset)) + 
  geom_point(alpha=0.75, size=1) +
  theme_light() + 
  theme.text.size + theme(legend.position = 'top') +
  labs(y="Cumulative fraction of genomes", x="Proportion of sites masked to N") +
  scale_y_continuous(breaks=seq(0,1,0.1))

p.cumulative.Ncount.for.datset
```

\
BEAST 95% HPD calculations (provide more details for 520 dataset    )
```{r}
BEAST.median <- 1.28e-7
BEAST.95HPD <- c(1.07e-7, 1.48e-7)
SS14.aln.length <- 1139569


1/(BEAST.median * SS14.aln.length)
1/(BEAST.95HPD * SS14.aln.length)
```

\
\
Further evaluation of sublineage 6 (reviewer response) using ancestral reconstruction performed on the global TPA-only alignment/tree used in Beale 2021.

```{r}
TPA.treetime.ancestral.tree <- read.nexus(TPA.treetime.ancestral.tree.file)
TPA.treetime.ancestral.tree.data <- fortify(TPA.treetime.ancestral.tree)

ggtree(TPA.treetime.ancestral.tree) + geom_nodelab(size=2)

# Read in and process TPA-only vcf (to confirm sites are the same)
TPA.only.midpoint.treetime.ancestral.vcf <- read.vcfR(TPA.treetime.ancestral.vcf.file, verbose = FALSE)
TPA.only.midpoint.treetime.ancestral.vcf.fix <- getFIX(TPA.only.midpoint.treetime.ancestral.vcf)
TPA.only.midpoint.treetime.ancestral.vcf.fix <- data.frame(TPA.only.midpoint.treetime.ancestral.vcf.fix[,c(2,4,5)], stringsAsFactors = F)
TPA.only.midpoint.treetime.ancestral.vcf.fix$in.TPA.only <- "yes"
TPA.only.midpoint.treetime.ancestral.vcf.fix$Key <- 1:nrow(TPA.only.midpoint.treetime.ancestral.vcf.fix)

```

\
Extract genotype sites
```{r}
TPA.treetime.ancestral.vcf.gt <- extract_gt_tidy(TPA.only.midpoint.treetime.ancestral.vcf)

TPA.treetime.ancestral.vcf.gt.f <- plyr::join(TPA.treetime.ancestral.vcf.gt, TPA.only.midpoint.treetime.ancestral.vcf.fix[,c("Key","POS")], by="Key", type="left")

TPA.treetime.ancestral.vcf.gt.f$POS <- as.numeric(TPA.treetime.ancestral.vcf.gt.f$POS)
TPA.treetime.ancestral.vcf.gt.f$gt_GT <- as.numeric(TPA.treetime.ancestral.vcf.gt.f$gt_GT)

TPA.treetime.ancestral.vcf.gt.f.spread <- tidyr::spread(TPA.treetime.ancestral.vcf.gt.f[,c("POS","Indiv","gt_GT")], POS, gt_GT) 

```

Use snpEff to annotate multi-vcf, and then pull in annotations here
```{r}
TPA.snpEff <- read.table(TPA.snpEff.file,header = T, check.names = F, comment.char = "",sep="\t")

TPA.snpEff.filt <- TPA.snpEff[!(TPA.snpEff$`ANN[*].GENE`=="gene-TPASS_RS00040" & TPA.snpEff$`ANN[*].EFFECT`=="intragenic_variant"),]
TPA.snpEff.filt[TPA.snpEff.filt$`ANN[*].EFFECT`==".","ANN[*].EFFECT"] <- "intragenic_variant"


TPA.snpEff.filt %>% dplyr::group_by(`ANN[*].EFFECT`) %>% summarise(Count=n())
TPA.snpEff.filt %>% dplyr::group_by(`ANN[*].GENE`) %>% summarise(Count=n())
TPA.snpEff.filt %>% dplyr::group_by(`ANN[*].GENE`,`ANN[*].EFFECT`) %>% summarise(Count=n())

TPA.snpEff.filt.var.per.pos <- TPA.snpEff.filt %>% dplyr::group_by(POS) %>% summarise(Count=n())
TPA.snpEff.filt.var.per.pos.multi <- as.numeric(as.character(unlist(TPA.snpEff.filt.var.per.pos[TPA.snpEff.filt.var.per.pos$Count>1,"POS"])))

TPA.snpEff.filt[TPA.snpEff.filt$POS %in% TPA.snpEff.filt.var.per.pos.multi,]

```

\
Lets pull in gene function (where known) for these sites from the gff
```{r}
SS14.gff <- ape::read.gff(SS14.gff.file)
SS14.gff.cds <- SS14.gff[SS14.gff$type=="CDS",]

#### function to extract different fields from attributes column
getAttributeField <- function (x, field, attrsep = ";") {
     s = strsplit(x, split = attrsep, fixed = TRUE)
     sapply(s, function(atts) {
         a = strsplit(atts, split = "=", fixed = TRUE)
         m = match(field, sapply(a, "[", 1))
         if (!is.na(m)) {
             rv = a[[m]][2]
         }
         else {
             rv = as.character(NA)
         }
         return(rv)
     })
}
###
#getAttributeField(SS14.gff.cds$attributes, "Name")


# Extract attribute elements from gff
SS14.gff.cds$geneid <- gsub("gene\\-","",getAttributeField(SS14.gff.cds$attributes, "Parent"))
SS14.gff.cds$locus_tag <- getAttributeField(SS14.gff.cds$attributes, "locus_tag")
SS14.gff.cds$gene <- getAttributeField(SS14.gff.cds$attributes, "gene")
SS14.gff.cds$product <- getAttributeField(SS14.gff.cds$attributes, "product")
SS14.gff.cds$proteinid <- getAttributeField(SS14.gff.cds$attributes, "protein_id")
# create a merged locus_tag/gene the way snpEff does
SS14.gff.cds$geneid <- sapply(1:nrow(SS14.gff.cds), function(x) ifelse(is.na(SS14.gff.cds$gene[x]),SS14.gff.cds$locus_tag[x], SS14.gff.cds$gene[x]))
SS14.gff.cds$gene.coords <- paste0(SS14.gff.cds$start,":",SS14.gff.cds$end)

SS14.gff.cds
```

\
# read in snp classifications, and apply to discriminatory SNPs
\
Write this as a function. Takes 4 arguments:
- dataframe of snps for each sample in wide matrix format (e.g. TPA.treetime.ancestral.vcf.gt.f.spread)
- longform list of SNPs and possible alleles (e.g. TPA.treetime.ancestral.vcf.fix)
- variant annotations dataframe (e.g. TPA.snpEff.filt)
- a vector of two nodes in the tree to compare (e.g. tt.nodes.to.compare.SS14)
\
```{r}
extract_branch_site_allelic_functions <- function(allele.matrix.spread, snp.table, snp.annotation.table, nodes.list){
  # filter SNP matrix to only include the two nodes of interest
  discriminatory.sites1 <- allele.matrix.spread[allele.matrix.spread$Indiv %in% nodes.list,]
  discriminatory.sites2 <- tidyr::gather(discriminatory.sites1,POS,Gt,-Indiv) %>% 
  tidyr::spread(Indiv, Gt)
  # Filter SNPs under consideration to those that are different between the two nodes
  discriminatory.sites2 <- discriminatory.sites2[(discriminatory.sites2[,2]!=discriminatory.sites2[,3]),]
  discriminatory.sites2 <- discriminatory.sites2[order(as.numeric(discriminatory.sites2$POS)),]
  # merge in the details about alleles at each relevant SNP position
  discriminatory.sites2 <- plyr::join(discriminatory.sites2, snp.table,by=c('POS'), type='left')
  # deal with multi-allelic sites, and discriminate between them
  discriminatory.sites2$ALT.multi <- discriminatory.sites2$ALT
  discriminatory.sites2$ALT <- sapply(1:nrow(discriminatory.sites2), function(x) strsplit(discriminatory.sites2$ALT.multi[x],",")[[1]][sort(as.numeric(((discriminatory.sites2[x,c(2,3)]))))[2]])
  # merge in the annotation for the appropriate allele/SNPs
  discriminatory.sites2.snpeff <- plyr::join(snp.annotation.table[,c("POS","ALT","ANN[*].ALLELE","ANN[*].EFFECT","ANN[*].GENE","ANN[*].HGVS_C","ANN[*].HGVS_P")], discriminatory.sites2[,c("POS","REF","ALT",nodes.list)], type="right", by=c("POS","ALT"))
  discriminatory.sites2.snpeff[is.na(discriminatory.sites2.snpeff$`ANN[*].EFFECT`),"ANN[*].EFFECT"] <- "intragenic_variant"
  # return output
  return(discriminatory.sites2.snpeff)
}
```

\
```{r}
#tt.nodes.to.compare.SS14.vs.Nichols.TPA <- c("NODE_0000005","NODE_0000103")

#tt.nodes.to.compare.sublineage6.vs.MRCA.TPA <- c("NODE_0000003","NODE_0000002")
tt.nodes.to.compare.sublineage6.vs.MRCA.TPA <- c("NODE_0000001","NODE_0000002")

sublin6.vs.mrca.Nichols.branch_site_alleles.TPA <- extract_branch_site_allelic_functions(TPA.treetime.ancestral.vcf.gt.f.spread,TPA.only.midpoint.treetime.ancestral.vcf.fix,TPA.snpEff.filt, tt.nodes.to.compare.sublineage6.vs.MRCA.TPA)

sublin6.vs.mrca.Nichols.branch_site_alleles.TPA %>% dplyr::group_by(`ANN[*].EFFECT`) %>% dplyr::summarise(count=n())

paste0("All Variants: ", nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA))
paste0("Unique Sites: ", length(unique(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$POS)))
paste0("Synonymous Variants: ", nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA[sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$`ANN[*].EFFECT`=="synonymous_variant",]))
paste0("Non-Synonymous Variants: ", nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA[sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$`ANN[*].EFFECT`=="missense_variant",]))
paste0("Intragenic Variants :", nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA[sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$`ANN[*].EFFECT`=="intragenic_variant",]))



sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var <- c(0, sapply(2:nrow(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA) , function(x) as.numeric(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$POS[x]) - as.numeric(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$POS[x-1]))) 

mean(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var)
median(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var)
min(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var)
max(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA$dist.from.last.var)  

p.sublineage6.ancestral.SNPs.genomepos <- ggplot(sublin6.vs.mrca.Nichols.branch_site_alleles.TPA, aes(x=as.numeric(POS), y=dist.from.last.var)) + 
  geom_point(size=1, alpha=0.5) +
  #geom_bar(stat='identity', alpha=0.5) +
  #geom_line(alpha=0.1) +
  theme_light() + theme(text = element_text(size = 10)) +
  coord_cartesian(xlim=c(0,SS14.aln.length)) +
  scale_x_continuous(breaks=pretty) +
  scale_y_log10() +
  labs(x="SS14 Genome Position (NC_021508.1; (bp))", y="Distance of variant from previous variant site (bp)", title="Genome position of SNPs delineating Sublineage 6 from MRCA node")
  
p.sublineage6.ancestral.SNPs.genomepos



p.sublineage6.ancestral.SNPs.dist.between.histo <- sublin6.vs.mrca.Nichols.branch_site_alleles.TPA %>%
  ggplot(aes(x=dist.from.last.var)) + 
  scale_x_log10() +
  geom_histogram(bins=50) + 
  theme_light() + theme(text = element_text(size = 10)) +
  labs(x="Distance of variant from previous variant site (bp)", y="Count") + coord_flip()

p.sublineage6.ancestral.SNPs.dist.between.histo

plot_grid(p.sublineage6.ancestral.SNPs.genomepos, p.sublineage6.ancestral.SNPs.dist.between.histo + y.theme.strip , rel_widths = c(8,1), align = T)
```
\
\
Do some further analysis of the North East sublineage distributions. We have 35 samples collected from these regions, of which 17 were collected from 2014 onwards. Is sublineage 14 missing by chance (could we be missing it simply because we haven't collected enough samples) or is this more likely to reflect true uneven regional distributions?
```{r}
# How many genomes found in Northern regions before and after first detection of sublineage 14 in 2014?
 PHE.metadata.linked %>%
  dplyr::mutate(before2014=ifelse(year>=2014,"2014onwards", "pre2014")) %>%
  dplyr::filter(phe_centre %in% c("North East", "North West", "Yorkshire and Humber")) %>%
  dplyr::group_by(before2014) %>%
  dplyr::summarise(count=n())

# What are the proportions of different sublineages around the UK before and after 2014?
PHE.meta.post2014.sublin.fracs <- PHE.metadata.linked %>% 
  #dplyr::filter(year>=2014) %>%
  dplyr::mutate(before2014=ifelse(year>=2014,"2014onwards", "pre2014")) %>%
  dplyr::group_by(before2014, TPA.pinecone.sublineage) %>%
  dplyr::summarise(Count=n()) %>%
  dplyr::mutate(total.all=sum(Count)) %>%
  dplyr::mutate(fraction=Count/total.all) %>%
  dplyr::arrange(desc(TPA.pinecone.sublineage), .by_group=T) %>%
  dplyr::mutate(cum_fract = cumsum(fraction)) %>%
  dplyr::mutate(cum_fract.mid = cum_fract-(fraction/2)) %>%
  dplyr::mutate(Lineage.perc=(Count/sum(Count)*100))
PHE.meta.post2014.sublin.fracs 


# simulating poisson process r to work out how many samples we would expect in Northern England under poisson distribution

# What % of sublineage 14 samples are found in the total population?
post2014.sublin14.freq <- PHE.meta.post2014.sublin.fracs %>% filter(before2014=="2014onwards", TPA.pinecone.sublineage==14) %>% select(Lineage.perc) %>% pull()
 

# Simulate and plot a Poisson distribution of how many sublineage 14 samples we would expect to find if we randomly selected 17 samples at 22% 
data.frame(rpois=rpois(1000000, 17/(100/post2014.sublin14.freq))) %>%
  ggplot(aes(rpois)) + geom_histogram(binwidth=1) +
  scale_x_continuous(breaks=seq(0,20,2)) +
  theme_light() +
  labs(x="Samples Found", y="Simulation Count")

# What are the quantile distributions from that?
quantile(rpois(1000000, 17/(100/post2014.sublin14.freq)), probs=c(0.01, 0.05, 0.5, 0.95, 0.99))
median(rpois(1000000, 17/(100/post2014.sublin14.freq)))
mean(rpois(1000000, 17/(100/post2014.sublin14.freq)))

# What is the probability of finding no samples (assuming uniform unbiased coverage)?
data.frame(n=seq(0,20,1), dpois=sapply(seq(0,20,1), function(x) dpois(x, lambda=17/(100/post2014.sublin14.freq)))) %>% 
  ggplot(aes(x=n, y=dpois)) + 
  geom_bar(stat='identity') +
  scale_x_continuous(breaks=pretty) +
  theme_light() +
  labs(x="Samples Found", y="Probability")

paste("Probability of finding zero samples is ", round(dpois(0, lambda=17/(100/post2014.sublin14.freq)), 5)) 
```



